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1. Multi‐phenotype analyses of hemostatic traits with cardiovascular events reveal novel genetic associations

Author

Temprano‐Sagrera, Gerard, Sitlani, Colleen M, Bone, William P, Martin‐Bornez, Miguel, Voight, Benjamin F, Morrison, Alanna C, Damrauer, Scott M, de Vries, Paul S, Smith, Nicholas L, Sabater‐Lleal, Maria, Dehghan, Abbas, Heath, Adam S, Reiner, Alex P, Johnson, Andrew, Richmond, Anne, Peters, Annette, van Hylckama Vlieg, Astrid, McKnight, Barbara, Psaty, Bruce M, Hayward, Caroline, Ward‐Caviness, Cavin, O’Donnell, Christopher, Chasman, Daniel, Strachan, David P, Tregouet, David A, Mook‐Kanamori, Dennis, Gill, Dipender, Thibord, Florian, Asselbergs, Folkert W, Leebeek, Frank WG, Rosendaal, Frits R, Davies, Gail, Homuth, Georg, Temprano, Gerard, Campbell, Harry, Taylor, Herman A, Bressler, Jan, Huffman, Jennifer E, Rotter, Jerome I, Yao, Jie, Wilson, James F, Bis, Joshua C, Hahn, Julie M, Desch, Karl C, Wiggins, Kerri L, Raffield, Laura M, Bielak, Lawrence F, Yanek, Lisa R, Kleber, Marcus E, Mueller, Martina, Kavousi, Maryam, Mangino, Massimo, Liu, Melissa, Brown, Michael R, Conomos, Matthew P, Jhun, Min‐A, Chen, Ming‐Huei, de Maat, Moniek PM, Pankratz, Nathan, Peyser, Patricia A, Elliot, Paul, Wei, Peng, Wild, Philipp S, Morange, Pierre E, van der Harst, Pim, Yang, Qiong, Le, Ngoc‐Quynh, Marioni, Riccardo, Li, Ruifang, Cox, Simon R, Trompet, Stella, Felix, Stephan B, Völker, Uwe, Tang, Weihong, Koenig, Wolfgang, Jukema, J Wouter, Guo, Xiuqing, Lindstrom, Sara, Wang, Lu, Smith, Erin N, Gordon, William, de Andrade, Mariza, Brody, Jennifer A, Pattee, Jack W, Haessler, Jeffrey, Brumpton, Ben M, Chasman, Daniel I, Suchon, Pierre, Turman, Constance, Germain, Marine, MacDonald, James, and Braekkan, Sigrid K

Subjects
Genetics, Biotechnology, Human Genome, Atherosclerosis, Prevention, Cardiovascular, Hematology, 2.1 Biological and endogenous factors, Aetiology, Cardiovascular Diseases, Factor XI, Genetic Predisposition to Disease, Genome-Wide Association Study, Hemostasis, Hemostatics, Humans, Phenotype, Polymorphism, Single Nucleotide, Tissue Plasminogen Activator, blood coagulation, cardiovascular diseases, genetic pleiotropy, genome-wide association study, hemostasis, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Cardiovascular System & Hematology
Abstract

BackgroundMulti-phenotype analysis of genetically correlated phenotypes can increase the statistical power to detect loci associated with multiple traits, leading to the discovery of novel loci. This is the first study to date to comprehensively analyze the shared genetic effects within different hemostatic traits, and between these and their associated disease outcomes.ObjectivesTo discover novel genetic associations by combining summary data of correlated hemostatic traits and disease events.MethodsSummary statistics from genome wide-association studies (GWAS) from seven hemostatic traits (factor VII [FVII], factor VIII [FVIII], von Willebrand factor [VWF] factor XI [FXI], fibrinogen, tissue plasminogen activator [tPA], plasminogen activator inhibitor 1 [PAI-1]) and three major cardiovascular (CV) events (venous thromboembolism [VTE], coronary artery disease [CAD], ischemic stroke [IS]), were combined in 27 multi-trait combinations using metaUSAT. Genetic correlations between phenotypes were calculated using Linkage Disequilibrium Score Regression (LDSC). Newly associated loci were investigated for colocalization. We considered a significance threshold of 1.85 × 10-9 obtained after applying Bonferroni correction for the number of multi-trait combinations performed (n = 27).ResultsAcross the 27 multi-trait analyses, we found 4 novel pleiotropic loci (XXYLT1, KNG1, SUGP1/MAU2, TBL2/MLXIPL) that were not significant in the original individual datasets, were not described in previous GWAS for the individual traits, and that presented a common associated variant between the studied phenotypes.ConclusionsThe discovery of four novel loci contributes to the understanding of the relationship between hemostasis and CV events and elucidate common genetic factors between these traits.

Published
2022

2. A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels

Author

Abe, Namiko, Abecasis, Gonçalo, Aguet, Francois, Albert, Christine, Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Ardlie, Kristin, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Ayas, Najib, Balasubramanian, Adithya, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Barwick, Lucas, Beaty, Terri, Beck, Gerald, Becker, Diane, Becker, Lewis, Beer, Rebecca, Beitelshees, Amber, Benjamin, Emelia, Benos, Takis, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Blue, Nathan, Boerwinkle, Eric, Bowden, Donald W., Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Brown, Deborah, Bunting, Karen, Burchard, Esteban, Bustamante, Carlos, Buth, Erin, Cade, Brian, Cardwell, Jonathan, Carey, Vincent, Carrier, Julie, Carson, April P., Carty, Cara, Casaburi, Richard, Casas Romero, Juan P, Casella, James, Castaldi, Peter, Chaffin, Mark, Chang, Christy, Chang, Yi-Cheng, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Ida Chen, Yii-Der, Cho, Michael, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Chung, Ren-Hua, Clish, Clary, Comhair, Suzy, Conomos, Matthew, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, de las Fuentes, Lisa, de Vries, Paul, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Dinh, Huyen, Doddapaneni, Harsha, Duan, Qing, Dugan-Perez, Shannon, Duggirala, Ravi, Durda, Jon Peter, Dutcher, Susan K., Eaton, Charles, Ekunwe, Lynette, El Boueiz, Adel, Ellinor, Patrick, Emery, Leslie, Erzurum, Serpil, Farber, Charles, Farek, Jesse, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Frazar, Chris, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Shanshan, Gao, Yan, Gass, Margery, Geiger, Heather, Gelb, Bruce, Geraci, Mark, Germer, Soren, Gerszten, Robert, Ghosh, Auyon, Gibbs, Richard, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Graw, Sharon, Gray, Kathryn J., Grine, Daniel, Gross, Colin, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Gupta, Namrata, Haessler, Jeff, Hall, Michael, Han, Yi, Hanly, Patrick, Harris, Daniel, Hawley, Nicola L., He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hobbs, Brian, Hokanson, John, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Hu, Jianhong, Hung, Yi-Jen, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Khan, Ziad, Kim, Wonji, Kimoff, John, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Kramer, Holly, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Jiwon, Lee, Sandra, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Dan, Lewis, Joshua, Li, Xiaohui, Li, Yun, Lin, Henry, Lin, Honghuang, Lin, Xihong, Liu, Simin, Liu, Yongmei, Liu, Yu, Loos, Ruth J. F., Lubitz, Steven, Lunetta, Kathryn, Luo, James, Magalang, Ulysses, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manning, Alisa, Manson, JoAnn, Martin, Lisa, Marton, Melissa, Mathai, Susan, Mathias, Rasika, May, Susanne, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, McGoldrick, Daniel, McHugh, Caitlin, McNeil, Becky, Mei, Hao, Meigs, James, Menon, Vipin, Mestroni, Luisa, Metcalf, Ginger, Meyers, Deborah A, Mignot, Emmanuel, Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L, Mitchell, Braxton D., Moll, Matt, Momin, Zeineen, Montasser, May E., Montgomery, Courtney, Muzny, Donna, Mychaleckyj, Josyf C, Nadkarni, Girish, Naik, Rakhi, Naseri, Take, Natarajan, Pradeep, Nekhai, Sergei, Nelson, Sarah C., Neltner, Bonnie, Nessner, Caitlin, Nickerson, Deborah, Nkechinyere, Osuji, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Okwuonu, Geoffrey, Pack, Allan, Paik, David T., Palmer, Nicholette, Pankow, James, Papanicolaou, George, Parker, Cora, Peloso, Gina, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S, Pleiness, Jacob, Pollin, Toni, Post, Wendy, Becker, Julia Powers, Boorgula, Meher Preethi, Preuss, Michael, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Rajendran, Mahitha, Ramachandran, Vasan S., Rao, D. C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Reeves, Catherine, Regan, Elizabeth, Reiner, Alex, Reupena, Muagututi‘a Sefuiva, Rice, Ken, Rich, Stephen, Robillard, Rebecca, Robine, Nicolas, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Runnels, Alexi, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sabino, Ester Cerdeira, Saleheen, Danish, Salimi, Shabnam, Salvi, Sejal, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay G., Santibanez, Jireh, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sériès, Frédéric, Sheehan, Vivien, Sherman, Stephanie L., Shetty, Amol, Shetty, Aniket, Hui-Heng Sheu, Wayne, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Skomro, Robert, Smith, Albert Vernon, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Snyder, Michael, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne M., Storm, Garrett, Streeten, Elizabeth, Su, Jessica Lasky, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent D., Taylor, Matthew, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Threlkeld, Machiko, Tinker, Lesley, Tirschwell, David, Tishkoff, Sarah, Tiwari, Hemant, Tong, Catherine, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, Van Den Berg, David, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wang, Fei Fei, Wang, Heming, Wang, Jiongming, Watson, Karol, Watt, Jennifer, Weeks, Daniel E., Weinstock, Joshua, Weir, Bruce, Weiss, Scott T, Weng, Lu-Chen, Wessel, Jennifer, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Wilson, Carla, Wilson, James, Winterkorn, Lara, Wong, Quenna, Wu, Joseph, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yu, Ketian, Zekavat, Seyedeh Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zhu, Xiaofeng, Ziv, Elad, Zody, Michael, Zoellner, Sebastian, Lindstrom, Sara, Wang, Lu, Smith, Erin N., Gordon, William, van Hylckama Vlieg, Astrid, Brody, Jennifer A., Pattee, Jack W., Haessler, Jeffrey, Brumpton, Ben M., Chasman, Daniel I., Suchon, Pierre, Chen, Ming-Huei, Turman, Constance, Germain, Marine, Wiggins, Kerri L., MacDonald, James, Braekkan, Sigrid K., Armasu, Sebastian M., Pankratz, Nathan, Jackson, Rabecca D., Nielsen, Jonas B., Giulianini, Franco, Puurunen, Marja K., Ibrahim, Manal, Heckbert, Susan R., Bammler, Theo K., Frazer, Kelly A., McCauley, Bryan M., Taylor, Kent, Pankow, James S., Reiner, Alexander P., Gabrielsen, Maiken E., Deleuze, Jean-François, O'Donnell, Chris J., Kim, Jihye, McKnight, Barbara, Kraft, Peter, Hansen, John-Bjarne, Rosendaal, Frits R., Heit, John A., Psaty, Bruce M., Tang, Weihong, Hveem, Kristian, Ridker, Paul M., Morange, Pierre-Emmanuel, Johnson, Andrew D., Kabrhel, Christopher, AlexandreTrégouët, David, Smith, Nicholas L., de Vries, Paul S., Reventun, Paula, Brown, Michael R., Heath, Adam S., Huffman, Jennifer E., Le, Ngoc-Quynh, Bebo, Allison, Temprano-Sagrera, Gerard, Raffield, Laura M., Ozel, Ayse Bilge, Thibord, Florian, Lewis, Joshua P., Rodriguez, Benjamin A. T., Polasek, Ozren, Yanek, Lisa R., Carrasquilla, German D., Marioni, Riccardo E., Kleber, Marcus E., Trégouët, David-Alexandre, Yao, Jie, Li-Gao, Ruifang, Joshi, Peter K., Trompet, Stella, Martinez-Perez, Angel, Ghanbari, Mohsen, Howard, Tom E., Reiner, Alex P., Arvanitis, Marios, Ryan, Kathleen A., Bartz, Traci M., Rudan, Igor, Faraday, Nauder, Linneberg, Allan, Davies, Gail, Delgado, Graciela E., Klaric, Lucija, Noordam, Raymond, van Rooij, Frank, Curran, Joanne E., Wheeler, Marsha M., Osburn, William O., O'Connell, Jeffrey R., Beswick, Andrew, Kolcic, Ivana, Souto, Juan Carlos, Becker, Lewis C., Hansen, Torben, Doyle, Margaret F., Harris, Sarah E., Moissl, Angela P., Rich, Stephen S., Campbell, Harry, Stott, David J., Soria, Jose Manuel, de Maat, Moniek P. M., Brody, Lawrence C., Auer, Paul L., Ben-Shlomo, Yoav, Hayward, Caroline, Mathias, Rasika A., Kilpeläinen, Tuomas O., Lange, Leslie A., Cox, Simon R., März, Winfried, Rotter, Jerome I., Mook-Kanamori, Dennis O., Wilson, James F., van der Harst, Pim, Jukema, J. Wouter, Ikram, M. Arfan, Desch, Karl C., Sabater-Lleal, Maria, Lowenstein, Charles J., and Morrison, Alanna C.

Published
2024
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14. Fibrinogen gamma gene rs2066865 and risk of cancer-related venous thromboembolism

Author

Paulsen, Benedikte, Skille, Hanne, Smith, Erin N, Hveem, Kristian, Gabrielsen, Maiken E, Brækkan, Sigrid K, Rosendaal, Frits R, Frazer, Kelly A, Gran, Olga V, and Hansen, John-Bjarne

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Genetics, Rare Diseases, Cancer, Hematology, Prevention, Clinical Research, Aetiology, 2.1 Biological and endogenous factors, Alleles, Fibrinogen, Humans, Incidence, Neoplasms, Risk Factors, Venous Thromboembolism, Cardiorespiratory Medicine and Haematology, Immunology, Cardiovascular medicine and haematology
Abstract

Venous thromboembolism (VTE) is a frequent complication in patients with cancer. Homozygous carriers of the fibrinogen gamma gene (FGG) rs2066865 have a moderately increased risk of VTE, but the effect of the FGG variant in cancer is unknown. We aimed to investigate the effect of the FGG variant and active cancer on the risk of VTE. Cases with incident VTE (n=640) and a randomly selected age-weighted sub-cohort (n=3,734) were derived from a population-based cohort (the Tromsø study). Cox-regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for VTE according to categories of cancer and FGG In those without cancer, homozygosity at the FGG variant was associated with a 70% (HR 1.7, 95% CI: 1.2-2.3) increased risk of VTE compared to non-carriers. Cancer patients homozygous for the FGG variant had a two-fold (HR 2.0, 95% CI: 1.1-3.6) higher risk of VTE than cancer patients without the variant. Moreover, the six-months cumulative incidence of VTE among cancer patients was 6.4% (95% CI: 3.5-11.6) in homozygous carriers of FGG and 3.1% (95% CI: 2.3-4.7) in those without risk alleles. A synergistic effect was observed between rs2066865 and active cancer on the risk of VTE (synergy index: 1.81, 95% CI: 1.02-3.21, attributable proportion: 0.43, 95% CI: 0.11-0.74). In conclusion, homozygosity at the FGG variant and active cancer yielded a synergistic effect on the risk of VTE.

Published
2020

19. A large‐scale exome array analysis of venous thromboembolism

Author

Lindström, Sara, Brody, Jennifer A, Turman, Constance, Germain, Marine, Bartz, Traci M, Smith, Erin N, Chen, Ming‐Huei, Puurunen, Marja, Chasman, Daniel, Hassler, Jeffrey, Pankratz, Nathan, Basu, Saonli, Guan, Weihua, Gyorgy, Beata, Ibrahim, Manal, Empana, Jean‐Philippe, Olaso, Robert, Jackson, Rebecca, Brækkan, Sigrid K, McKnight, Barbara, Deleuze, Jean‐Francois, O’Donnell, Cristopher J, Jouven, Xavier, Frazer, Kelly A, Psaty, Bruce M, Wiggins, Kerri L, Taylor, Kent, Reiner, Alexander P, Heckbert, Susan R, Kooperberg, Charles, Ridker, Paul, Hansen, John‐Bjarne, Tang, Weihong, Johnson, Andrew D, Morange, Pierre‐Emmanuel, Trégouët, David A, Kraft, Peter, Smith, Nicholas L, Kabrhel, Christopher, and Consortium, on behalf of the INVENT

Subjects
Prevention, Clinical Research, Biotechnology, Human Genome, Genetics, 2.1 Biological and endogenous factors, Aetiology, Black or African American, Alleles, Case-Control Studies, Exome, Female, Gene Frequency, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, High-Throughput Nucleotide Sequencing, Humans, Male, Microarray Analysis, Odds Ratio, Polymorphism, Single Nucleotide, Sample Size, Venous Thromboembolism, exome, genetic association, venous thromboembolism, INVENT Consortium, Public Health and Health Services, Epidemiology
Abstract

Although recent Genome-Wide Association Studies have identified novel associations for common variants, there has been no comprehensive exome-wide search for low-frequency variants that affect the risk of venous thromboembolism (VTE). We conducted a meta-analysis of 11 studies comprising 8,332 cases and 16,087 controls of European ancestry and 382 cases and 1,476 controls of African American ancestry genotyped with the Illumina HumanExome BeadChip. We used the seqMeta package in R to conduct single variant and gene-based rare variant tests. In the single variant analysis, we limited our analysis to the 64,794 variants with at least 40 minor alleles across studies (minor allele frequency [MAF] ~0.08%). We confirmed associations with previously identified VTE loci, including ABO, F5, F11, and FGA. After adjusting for multiple testing, we observed no novel significant findings in single variant or gene-based analysis. Given our sample size, we had greater than 80% power to detect minimum odds ratios greater than 1.5 and 1.8 for a single variant with MAF of 0.01 and 0.005, respectively. Larger studies and sequence data may be needed to identify novel low-frequency and rare variants associated with VTE risk.

Published
2019

26. Association of Traditional Cardiovascular Risk Factors With Venous Thromboembolism

Author

Mahmoodi, Bakhtawar K, Cushman, Mary, Anne Næss, Inger, Allison, Matthew A, Bos, Willem J, Brækkan, Sigrid K, Cannegieter, Suzanne C, Gansevoort, Ron T, Gona, Philimon N, Hammerstrøm, Jens, Hansen, John-Bjarne, Heckbert, Susan, Holst, Anders G, Lakoski, Susan G, Lutsey, Pamela L, Manson, JoAnn E, Martin, Lisa W, Matsushita, Kunihiro, Meijer, Karina, Overvad, Kim, Prescott, Eva, Puurunen, Marja, Rossouw, Jacques E, Sang, Yingying, Severinsen, Marianne T, Ten Berg, Jur, Folsom, Aaron R, and Zakai, Neil A

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Tobacco, Hematology, Prevention, Cardiovascular, Clinical Research, Tobacco Smoke and Health, Good Health and Well Being, Age Factors, Blood Pressure, Body Mass Index, Diabetes Complications, Humans, Hyperlipidemias, Hypertension, Lipids, Proportional Hazards Models, Prospective Studies, Pulmonary Embolism, Risk Factors, Sex Factors, Smoking, Venous Thromboembolism, Venous Thrombosis, cardiovascular disease, diabetes mellitus, hyperlipidemia, hypertension, risk factors, smoking, venous thromboembolism, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences, Sports science and exercise
Abstract

BackgroundMuch controversy surrounds the association of traditional cardiovascular disease risk factors with venous thromboembolism (VTE).MethodsWe performed an individual level random-effect meta-analysis including 9 prospective studies with measured baseline cardiovascular disease risk factors and validated VTE events. Definitions were harmonized across studies. Traditional cardiovascular disease risk factors were modeled categorically and continuously using restricted cubic splines. Estimates were obtained for overall VTE, provoked VTE (ie, VTE occurring in the presence of 1 or more established VTE risk factors), and unprovoked VTE, pulmonary embolism, and deep-vein thrombosis.ResultsThe studies included 244 865 participants with 4910 VTE events occurring during a mean follow-up of 4.7 to 19.7 years per study. Age, sex, and body mass index-adjusted hazard ratios for overall VTE were 0.98 (95% confidence interval [CI]: 0.89-1.07) for hypertension, 0.97 (95% CI: 0.88-1.08) for hyperlipidemia, 1.01 (95% CI: 0.89-1.15) for diabetes mellitus, and 1.19 (95% CI: 1.08-1.32) for current smoking. After full adjustment, these estimates were numerically similar. When modeled continuously, an inverse association was observed for systolic blood pressure (hazard ratio=0.79 [95% CI: 0.68-0.92] at systolic blood pressure 160 vs 110 mm Hg) but not for diastolic blood pressure or lipid measures with VTE. An important finding from VTE subtype analyses was that cigarette smoking was associated with provoked but not unprovoked VTE. Fully adjusted hazard ratios for the associations of current smoking with provoked and unprovoked VTE were 1.36 (95% CI: 1.22-1.52) and 1.08 (95% CI: 0.90-1.29), respectively.ConclusionsExcept for the association between cigarette smoking and provoked VTE, which is potentially mediated through comorbid conditions such as cancer, the modifiable traditional cardiovascular disease risk factors are not associated with increased VTE risk. Higher systolic blood pressure showed an inverse association with VTE.

Published
2017

31. Joint effects of cancer and variants in the factor 5 gene on the risk of venous thromboembolism

Author

Gran, Olga V, Smith, Erin N, Brækkan, Sigrid K, Jensvoll, Hilde, Solomon, Terry, Hindberg, Kristian, Wilsgaard, Tom, Rosendaal, Frits R, Frazer, Kelly A, and Hansen, John-Bjarne

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Prevention, Clinical Research, Cancer, Genetics, 2.1 Biological and endogenous factors, Aetiology, Adult, Aged, Aged, 80 and over, Alleles, Factor V, Female, Gene Frequency, Genetic Variation, Genotype, Humans, Incidence, Kaplan-Meier Estimate, Male, Middle Aged, Neoplasms, Population Surveillance, Proportional Hazards Models, Risk, Venous Thromboembolism, Cardiorespiratory Medicine and Haematology, Immunology, Cardiovascular medicine and haematology
Abstract

Venous thromboembolism occurs frequently in cancer patients. Two variants in the factor 5 gene (F5), rs6025 encoding for the factor V Leiden mutation R506Q, and rs4524 encoding K858R, have been found to be associated with venous thromboembolism. We assessed the joint effect of active cancer and these two F5 variants on venous thromboembolism risk in a case-cohort study. Cases with a first venous thromboembolism (n=609) and a randomly selected age-weighted cohort (n=1,691) were sampled from the general population in Tromsø, Norway. Venous thromboembolism was classified as cancer-related if it occurred in the period 6 months before to 2 years after a diagnosis of cancer. Active cancer was associated with an 8.9-fold higher risk of venous thromboembolism (95% CI 7.2-10.9). The risk of cancer-related venous thromboembolism was 16.7-fold (95% CI 9.9-28.0) higher in subjects heterozygous for rs6025 compared with non-carriers of this variant without active cancer. In subjects with active cancer the risk of venous thromboembolism was 15.9-fold higher (95% CI 9.1-27.9) in those with one risk allele at rs4524, and 21.1-fold (95% CI 12.4-35.8) higher in those with two risk alleles compared with non-carriers without active cancer. A synergistic interaction was observed between active cancer and factor V Leiden (relative excess risk due to interaction 7.0; 95% CI 0.5-14.4) and rs4524 (relative excess risk due to interaction 15.0; 95% CI 7.5-29.2). The incidence of venous thromboembolism during the initial 6 months following a diagnosis of cancer was particularly high in subjects with risk alleles at these loci. This implies that the combination of cancer and F5 variants synergistically increases venous thromboembolism risk.

Published
2016

32. Associations Between Common and Rare Exonic Genetic Variants and Serum Levels of 20 Cardiovascular-Related Proteins

Author

Solomon, Terry, Smith, Erin N, Matsui, Hiroko, Braekkan, Sigrid K, Wilsgaard, Tom, Njølstad, Inger, Mathiesen, Ellisiv B, Hansen, John-Bjarne, and Frazer, Kelly A

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Genetics, Cardiovascular, Biotechnology, 1.1 Normal biological development and functioning, Underpinning research, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Blood Proteins, Case-Control Studies, Computational Biology, Databases, Genetic, Exons, Gene Frequency, Genetic Markers, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Natriuretic Peptide, Brain, Norway, Phenotype, Prospective Studies, Protein Precursors, Quantitative Trait Loci, Risk Factors, Venous Thromboembolism, biomarker, coronary artery disease, exome, human, protein, venous thromboembolism, INVENT Consortium, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundGenetic variation can be used to study causal relationships between biomarkers and diseases. Here, we identify new common and rare genetic variants associated with cardiovascular-related protein levels (protein quantitative trait loci [pQTLs]). We functionally annotate these pQTLs, predict and experimentally confirm a novel molecular interaction, and determine which pQTLs are associated with diseases and physiological phenotypes.Methods and resultsAs part of a larger case-control study of venous thromboembolism, serum levels of 51 proteins implicated in cardiovascular diseases were measured in 330 individuals from the Tromsø Study. Exonic genetic variation near each protein's respective gene (cis) was identified using sequencing and arrays. Using single site and gene-based tests, we identified 27 genetic associations between pQTLs and the serum levels of 20 proteins: 14 associated with common variation in cis, of which 6 are novel (ie, not previously reported); 7 associations with rare variants in cis, of which 4 are novel; and 6 associations in trans. Of the 20 proteins, 15 were associated with single sites and 7 with rare variants. cis-pQTLs for kallikrein and F12 also show trans associations for proteins (uPAR, kininogen) known to be cleaved by kallikrein and with NTproBNP. We experimentally demonstrate that kallikrein can cleave proBNP (NTproBNP precursor) in vitro. Nine of the pQTLs have previously identified associations with 17 disease and physiological phenotypes.ConclusionsWe have identified cis and trans genetic variation associated with the serum levels of 20 proteins and utilized these pQTLs to study molecular mechanisms underlying disease and physiological phenotypes.

Published
2016

33. Associations Between Common and Rare Exonic Genetic Variants and Serum Levels of 20 Cardiovascular-Related Proteins: The Tromsø Study.

Author

Solomon, Terry, Smith, Erin N, Matsui, Hiroko, Braekkan, Sigrid K, INVENT Consortium, Wilsgaard, Tom, Njølstad, Inger, Mathiesen, Ellisiv B, Hansen, John-Bjarne, and Frazer, Kelly A

Subjects
INVENT Consortium, Humans, Genetic Predisposition to Disease, Natriuretic Peptide, Brain, Blood Proteins, Protein Precursors, Genetic Markers, Risk Factors, Case-Control Studies, Prospective Studies, Computational Biology, Gene Frequency, Phenotype, Quantitative Trait Loci, Exons, Databases, Genetic, Norway, Venous Thromboembolism, Genetic Variation, Genome-Wide Association Study, biomarker, coronary artery disease, exome, human, protein, venous thromboembolism, Natriuretic Peptide, Brain, Databases, Genetic, Genetics, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology
Abstract

BackgroundGenetic variation can be used to study causal relationships between biomarkers and diseases. Here, we identify new common and rare genetic variants associated with cardiovascular-related protein levels (protein quantitative trait loci [pQTLs]). We functionally annotate these pQTLs, predict and experimentally confirm a novel molecular interaction, and determine which pQTLs are associated with diseases and physiological phenotypes.Methods and resultsAs part of a larger case-control study of venous thromboembolism, serum levels of 51 proteins implicated in cardiovascular diseases were measured in 330 individuals from the Tromsø Study. Exonic genetic variation near each protein's respective gene (cis) was identified using sequencing and arrays. Using single site and gene-based tests, we identified 27 genetic associations between pQTLs and the serum levels of 20 proteins: 14 associated with common variation in cis, of which 6 are novel (ie, not previously reported); 7 associations with rare variants in cis, of which 4 are novel; and 6 associations in trans. Of the 20 proteins, 15 were associated with single sites and 7 with rare variants. cis-pQTLs for kallikrein and F12 also show trans associations for proteins (uPAR, kininogen) known to be cleaved by kallikrein and with NTproBNP. We experimentally demonstrate that kallikrein can cleave proBNP (NTproBNP precursor) in vitro. Nine of the pQTLs have previously identified associations with 17 disease and physiological phenotypes.ConclusionsWe have identified cis and trans genetic variation associated with the serum levels of 20 proteins and utilized these pQTLs to study molecular mechanisms underlying disease and physiological phenotypes.

Published
2016

36. Genomic and transcriptomic association studies identify 16 novel susceptibility loci for venous thromboembolism

Author

Lindström, Sara, Wang, Lu, Smith, Erin N., Gordon, William, van Hylckama Vlieg, Astrid, de Andrade, Mariza, Brody, Jennifer A., Pattee, Jack W., Haessler, Jeffrey, Brumpton, Ben M., Chasman, Daniel I., Suchon, Pierre, Chen, Ming-Huei, Turman, Constance, Germain, Marine, Wiggins, Kerri L., MacDonald, James, Braekkan, Sigrid K., Armasu, Sebastian M., Pankratz, Nathan, Jackson, Rebecca D., Nielsen, Jonas B., Giulianini, Franco, Puurunen, Marja K., Ibrahim, Manal, Heckbert, Susan R., Damrauer, Scott M., Natarajan, Pradeep, Klarin, Derek, de Vries, Paul S., Sabater-Lleal, Maria, Huffman, Jennifer E., Bammler, Theo K., Frazer, Kelly A., McCauley, Bryan M., Taylor, Kent, Pankow, James S., Reiner, Alexander P., Gabrielsen, Maiken E., Deleuze, Jean-François, O'Donnell, Chris J., Kim, Jihye, McKnight, Barbara, Kraft, Peter, Hansen, John-Bjarne, Rosendaal, Frits R., Heit, John A., Psaty, Bruce M., Tang, Weihong, Kooperberg, Charles, Hveem, Kristian, Ridker, Paul M., Morange, Pierre-Emmanuel, Johnson, Andrew D., Kabrhel, Christopher, Trégouët, David-Alexandre, and Smith, Nicholas L.

Published
2019
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38. Effective filtering strategies to improve data quality from population-based whole exome sequencing studies

Author

Carson, Andrew R, Smith, Erin N, Matsui, Hiroko, Brækkan, Sigrid K, Jepsen, Kristen, Hansen, John-Bjarne, and Frazer, Kelly A

Subjects
Biological Sciences, Genetics, Human Genome, Exome, Genotype, High-Throughput Nucleotide Sequencing, Humans, Polymorphism, Single Nucleotide, Sequence Analysis, DNA, Next generation sequencing, Single nucleotide variants, Genotyping, Imputation, Genomics, Mathematical Sciences, Information and Computing Sciences, Bioinformatics, Biological sciences, Information and computing sciences, Mathematical sciences
Abstract

BackgroundGenotypes generated in next generation sequencing studies contain errors which can significantly impact the power to detect signals in common and rare variant association tests. These genotyping errors are not explicitly filtered by the standard GATK Variant Quality Score Recalibration (VQSR) tool and thus remain a source of errors in whole exome sequencing (WES) projects that follow GATK's recommended best practices. Therefore, additional data filtering methods are required to effectively remove these errors before performing association analyses with complex phenotypes. Here we empirically derive thresholds for genotype and variant filters that, when used in conjunction with the VQSR tool, achieve higher data quality than when using VQSR alone.ResultsThe detailed filtering strategies improve the concordance of sequenced genotypes with array genotypes from 99.33% to 99.77%; improve the percent of discordant genotypes removed from 10.5% to 69.5%; and improve the Ti/Tv ratio from 2.63 to 2.75. We also demonstrate that managing batch effects by separating samples based on different target capture and sequencing chemistry protocols results in a final data set containing 40.9% more high-quality variants. In addition, imputation is an important component of WES studies and is used to estimate common variant genotypes to generate additional markers for association analyses. As such, we demonstrate filtering methods for imputed data that improve genotype concordance from 79.3% to 99.8% while removing 99.5% of discordant genotypes.ConclusionsThe described filtering methods are advantageous for large population-based WES studies designed to identify common and rare variation associated with complex diseases. Compared to data processed through standard practices, these strategies result in substantially higher quality data for common and rare association analyses.

Published
2014

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