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1. Adherence of Mobile App-Based Surveys and Comparison With Traditional Surveys: eCohort Study

Author

Pathiravasan, Chathurangi H, Zhang, Yuankai, Trinquart, Ludovic, Benjamin, Emelia J, Borrelli, Belinda, McManus, David D, Kheterpal, Vik, Lin, Honghuang, Sardana, Mayank, Hammond, Michael M, Spartano, Nicole L, Dunn, Amy L, Schramm, Eric, Nowak, Christopher, Manders, Emily S, Liu, Hongshan, Kornej, Jelena, Liu, Chunyu, and Murabito, Joanne M

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundeCohort studies offer an efficient approach for data collection. However, eCohort studies are challenged by volunteer bias and low adherence. We designed an eCohort embedded in the Framingham Heart Study (eFHS) to address these challenges and to compare the digital data to traditional data collection. ObjectiveThe aim of this study was to evaluate adherence of the eFHS app-based surveys deployed at baseline (time of enrollment in the eCohort) and every 3 months up to 1 year, and to compare baseline digital surveys with surveys collected at the research center. MethodsWe defined adherence rates as the proportion of participants who completed at least one survey at a given 3-month period and computed adherence rates for each 3-month period. To evaluate agreement, we compared several baseline measures obtained in the eFHS app survey to those obtained at the in-person research center exam using the concordance correlation coefficient (CCC). ResultsAmong the 1948 eFHS participants (mean age 53, SD 9 years; 57% women), we found high adherence to baseline surveys (89%) and a decrease in adherence over time (58% at 3 months, 52% at 6 months, 41% at 9 months, and 40% at 12 months). eFHS participants who returned surveys were more likely to be women (adjusted odds ratio [aOR] 1.58, 95% CI 1.18-2.11) and less likely to be smokers (aOR 0.53, 95% CI 0.32-0.90). Compared to in-person exam data, we observed moderate agreement for baseline app-based surveys of the Physical Activity Index (mean difference 2.27, CCC=0.56), and high agreement for average drinks per week (mean difference 0.54, CCC=0.82) and depressive symptoms scores (mean difference 0.03, CCC=0.77). ConclusionsWe observed that eFHS participants had a high survey return at baseline and each 3-month survey period over the 12 months of follow up. We observed moderate to high agreement between digital and research center measures for several types of surveys, including physical activity, depressive symptoms, and alcohol use. Thus, this digital data collection mechanism is a promising tool to collect data related to cardiovascular disease and its risk factors.

Published
2021
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2. Comparison of On-Site Versus Remote Mobile Device Support in the Framingham Heart Study Using the Health eHeart Study for Digital Follow-up: Randomized Pilot Study Set Within an Observational Study Design

Author

Spartano, Nicole L, Lin, Honghuang, Sun, Fangui, Lunetta, Kathryn L, Trinquart, Ludovic, Valentino, Maureen, Manders, Emily S, Pletcher, Mark J, Marcus, Gregory M, McManus, David D, Benjamin, Emelia J, Fox, Caroline S, Olgin, Jeffrey E, and Murabito, Joanne M

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundNew electronic cohort (e-Cohort) study designs provide resource-effective methods for collecting participant data. It is unclear if implementing an e-Cohort study without direct, in-person participant contact can achieve successful participation rates. ObjectiveThe objective of this study was to compare 2 distinct enrollment methods for setting up mobile health (mHealth) devices and to assess the ongoing adherence to device use in an e-Cohort pilot study. MethodsWe coenrolled participants from the Framingham Heart Study (FHS) into the FHS–Health eHeart (HeH) pilot study, a digital cohort with infrastructure for collecting mHealth data. FHS participants who had an email address and smartphone were randomized to our FHS-HeH pilot study into 1 of 2 study arms: remote versus on-site support. We oversampled older adults (age ≥65 years), with a target of enrolling 20% of our sample as older adults. In the remote arm, participants received an email containing a link to enrollment website and, upon enrollment, were sent 4 smartphone-connectable sensor devices. Participants in the on-site arm were invited to visit an in-person FHS facility and were provided in-person support for enrollment and connecting the devices. Device data were tracked for at least 5 months. ResultsCompared with the individuals who declined, individuals who consented to our pilot study (on-site, n=101; remote, n=93) were more likely to be women, highly educated, and younger. In the on-site arm, the connection and initial use of devices was ≥20% higher than the remote arm (mean percent difference was 25% [95% CI 17-35] for activity monitor, 22% [95% CI 12-32] for blood pressure cuff, 20% [95% CI 10-30] for scale, and 43% [95% CI 30-55] for electrocardiogram), with device connection rates in the on-site arm of 99%, 95%, 95%, and 84%. Once connected, continued device use over the 5-month study period was similar between the study arms. ConclusionsOur pilot study demonstrated that the deployment of mobile devices among middle-aged and older adults in the context of an on-site clinic visit was associated with higher initial rates of device use as compared with offering only remote support. Once connected, the device use was similar in both groups.

Published
2019
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3. Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Author

Kentistou, Katherine A., Kaisinger, Lena R., Stankovic, Stasa, Vaudel, Marc, Mendes de Oliveira, Edson, Messina, Andrea, Walters, Robin G., Liu, Xiaoxi, Busch, Alexander S., Helgason, Hannes, Thompson, Deborah J., Santoni, Federico, Petricek, Konstantin M., Zouaghi, Yassine, Huang-Doran, Isabel, Gudbjartsson, Daniel F., Bratland, Eirik, Lin, Kuang, Gardner, Eugene J., Zhao, Yajie, Jia, Raina Y., Terao, Chikashi, Riggan, Marjorie J., Bolla, Manjeet K., Yazdanpanah, Mojgan, Yazdanpanah, Nahid, Bradfield, Jonathan P., Broer, Linda, Campbell, Archie, Chasman, Daniel I., Cousminer, Diana L., Franceschini, Nora, Franke, Lude H., Girotto, Giorgia, He, Chunyan, Järvelin, Marjo-Riitta, Joshi, Peter K., Kamatani, Yoichiro, Karlsson, Robert, Luan, Jian’an, Lunetta, Kathryn L., Mägi, Reedik, Mangino, Massimo, Medland, Sarah E., Meisinger, Christa, Noordam, Raymond, Nutile, Teresa, Concas, Maria Pina, Polašek, Ozren, Porcu, Eleonora, Ring, Susan M., Sala, Cinzia, Smith, Albert V., Tanaka, Toshiko, van der Most, Peter J., Vitart, Veronique, Wang, Carol A., Willemsen, Gonneke, Zygmunt, Marek, Ahearn, Thomas U., Andrulis, Irene L., Anton-Culver, Hoda, Antoniou, Antonis C., Auer, Paul L., Barnes, Catriona L. K., Beckmann, Matthias W., Berrington de Gonzalez, Amy, Bogdanova, Natalia V., Bojesen, Stig E., Brenner, Hermann, Buring, Julie E., Canzian, Federico, Chang-Claude, Jenny, Couch, Fergus J., Cox, Angela, Crisponi, Laura, Czene, Kamila, Daly, Mary B., Demerath, Ellen W., Dennis, Joe, Devilee, Peter, De Vivo, Immaculata, Dörk, Thilo, Dunning, Alison M., Dwek, Miriam, Eriksson, Johan G., Fasching, Peter A., Fernandez-Rhodes, Lindsay, Ferreli, Liana, Fletcher, Olivia, Gago-Dominguez, Manuela, García-Closas, Montserrat, García-Sáenz, José A., González-Neira, Anna, Grallert, Harald, Guénel, Pascal, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hakonarson, Hakon, Hart, Roger J., Hickey, Martha, Hooning, Maartje J., Hoppe, Reiner, Hopper, John L., Hottenga, Jouke-Jan, Hu, Frank B., Huebner, Hanna, Hunter, David J., Jernström, Helena, John, Esther M., Karasik, David, Khusnutdinova, Elza K., Kristensen, Vessela N., Lacey, James V., Lambrechts, Diether, Launer, Lenore J., Lind, Penelope A., Lindblom, Annika, Magnusson, Patrik K. E., Mannermaa, Arto, McCarthy, Mark I., Meitinger, Thomas, Menni, Cristina, Michailidou, Kyriaki, Millwood, Iona Y., Milne, Roger L., Montgomery, Grant W., Nevanlinna, Heli, Nolte, Ilja M., Nyholt, Dale R., Obi, Nadia, O’Brien, Katie M., Offit, Kenneth, Oldehinkel, Albertine J., Ostrowski, Sisse R., Palotie, Aarno, Pedersen, Ole B., Peters, Annette, Pianigiani, Giulia, Plaseska-Karanfilska, Dijana, Pouta, Anneli, Pozarickij, Alfred, Radice, Paolo, Rennert, Gad, Rosendaal, Frits R., Ruggiero, Daniela, Saloustros, Emmanouil, Sandler, Dale P., Schipf, Sabine, Schmidt, Carsten O., Schmidt, Marjanka K., Small, Kerrin, Spedicati, Beatrice, Stampfer, Meir, Stone, Jennifer, Tamimi, Rulla M., Teras, Lauren R., Tikkanen, Emmi, Turman, Constance, Vachon, Celine M., Wang, Qin, Winqvist, Robert, Wolk, Alicja, Zemel, Babette S., Zheng, Wei, van Dijk, Ko W., Alizadeh, Behrooz Z., Bandinelli, Stefania, Boerwinkle, Eric, Boomsma, Dorret I., Ciullo, Marina, Chenevix-Trench, Georgia, Cucca, Francesco, Esko, Tõnu, Gieger, Christian, Grant, Struan F. A., Gudnason, Vilmundur, Hayward, Caroline, Kolčić, Ivana, Kraft, Peter, Lawlor, Deborah A., Martin, Nicholas G., Nøhr, Ellen A., Pedersen, Nancy L., Pennell, Craig E., Ridker, Paul M., Robino, Antonietta, Snieder, Harold, Sovio, Ulla, Spector, Tim D., Stöckl, Doris, Sudlow, Cathie, Timpson, Nic J., Toniolo, Daniela, Uitterlinden, André, Ulivi, Sheila, Völzke, Henry, Wareham, Nicholas J., Widen, Elisabeth, Wilson, James F., Pharoah, Paul D. P., Li, Liming, Easton, Douglas F., Njølstad, Pål R., Sulem, Patrick, Murabito, Joanne M., Murray, Anna, Manousaki, Despoina, Juul, Anders, Erikstrup, Christian, Stefansson, Kari, Horikoshi, Momoko, Chen, Zhengming, Farooqi, I. Sadaf, Pitteloud, Nelly, Johansson, Stefan, Day, Felix R., Perry, John R. B., and Ong, Ken K.

Published
2024
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4. Proteomic architecture of frailty across the spectrum of cardiovascular disease.

Author

Perry, Andrew, Zhao, Shilin, Gajjar, Priya, Murthy, Venkatesh, Lehallier, Benoit, Miller, Patricia, Nair, Sangeeta, Neill, Colin, Carr, J, Fearon, William, Kapadia, Samir, Kumbhani, Dharam, Gillam, Linda, Lindenfeld, JoAnn, Farrell, Laurie, Marron, Megan, Tian, Qu, Newman, Anne, Murabito, Joanne, Gerszten, Robert, Nayor, Matthew, Lindman, Brian, Shah, Ravi, and Elmariah, Sammy

Subjects
cardiovascular disease, frailty, proteomics
Abstract

While frailty is a prominent risk factor in an aging population, the underlying biology of frailty is incompletely described. Here, we integrate 979 circulating proteins across a wide range of physiologies with 12 measures of frailty in a prospective discovery cohort of 809 individuals with severe aortic stenosis (AS) undergoing transcatheter aortic valve implantation. Our aim was to characterize the proteomic architecture of frailty in a highly susceptible population and study its relation to clinical outcome and systems-wide phenotypes to define potential novel, clinically relevant frailty biology. Proteomic signatures (specifically of physical function) were related to post-intervention outcome in AS, specifying pathways of innate immunity, cell growth/senescence, fibrosis/metabolism, and a host of proteins not widely described in human aging. In published cohorts, the frailty proteome displayed heterogeneous trajectories across age (20-100 years, age only explaining a small fraction of variance) and were associated with cardiac and non-cardiac phenotypes and outcomes across two broad validation cohorts (N > 35,000) over ≈2-3 decades. These findings suggest the importance of precision biomarkers of underlying multi-organ health status in age-related morbidity and frailty.

Published
2023

5. Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Author

Wang, Yuxuan, Selvaraj, Margaret, Li, Xihao, Li, Zilin, Holdcraft, Jacob, Arnett, Donna, Bis, Joshua, Blangero, John, Boerwinkle, Eric, Bowden, Donald, Cade, Brian, Carlson, Jenna, Carson, April, Chen, Yii-Der, Curran, Joanne, de Vries, Paul, Dutcher, Susan, Ellinor, Patrick, Floyd, James, Fornage, Myriam, Freedman, Barry, Gabriel, Stacey, Germer, Soren, Gibbs, Richard, Guo, Xiuqing, He, Jiang, Heard-Costa, Nancy, Hildalgo, Bertha, Hou, Lifang, Irvin, Marguerite, Joehanes, Roby, Kaplan, Robert, Kardia, Sharon, Kelly, Tanika, Kim, Ryan, Kooperberg, Charles, Kral, Brian, Levy, Daniel, Li, Changwei, Liu, Chunyu, Lloyd-Jone, Don, Loos, Ruth, Mahaney, Michael, Martin, Lisa, Mathias, Rasika, Minster, Ryan, Mitchell, Braxton, Montasser, May, Morrison, Alanna, Murabito, Joanne, Naseri, Take, OConnell, Jeffrey, Palmer, Nicholette, Preuss, Michael, Psaty, Bruce, Raffield, Laura, Rao, Dabeeru, Redline, Susan, Reiner, Alexander, Rich, Stephen, Ruepena, Muagututia, Sheu, Wayne, Smith, Jennifer, Smith, Albert, Tiwari, Hemant, Tsai, Michael, Viaud-Martinez, Karine, Wang, Zhe, Yanek, Lisa, Zhao, Wei, Lin, Xihong, Natarajan, Pradeep, Peloso, Gina, and Rotter, Jerome

Subjects
association, blood lipid, cholesterol, lncRNA, rare variants, whole-genome sequencing, Humans, RNA, Long Noncoding, Genome-Wide Association Study, Precision Medicine, Whole Genome Sequencing, Lipids, Polymorphism, Single Nucleotide
Abstract

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Published
2023

6. Whole-genome sequencing in 333,100 individuals reveals rare non-coding single variant and aggregate associations with height

Author

Hawkes, Gareth, Beaumont, Robin N., Li, Zilin, Mandla, Ravi, Li, Xihao, Albert, Christine M., Arnett, Donna K., Ashley-Koch, Allison E., Ashrani, Aneel A., Barnes, Kathleen C., Boerwinkle, Eric, Brody, Jennifer A., Carson, April P., Chami, Nathalie, Chen, Yii-Der Ida, Chung, Mina K., Curran, Joanne E., Darbar, Dawood, Ellinor, Patrick T., Fornage, Myrian, Gordeuk, Victor R., Guo, Xiuqing, He, Jiang, Hwu, Chii-Min, Kalyani, Rita R., Kaplan, Robert, Kardia, Sharon L. R., Kooperberg, Charles, Loos, Ruth J. F., Lubitz, Steven A., Minster, Ryan L., Naseri, Take, Viali, Satupa’itea, Mitchell, Braxton D., Murabito, Joanne M., Palmer, Nicholette D., Psaty, Bruce M., Redline, Susan, Shoemaker, M. Benjamin, Silverman, Edwin K., Telen, Marilyn J., Weiss, Scott T., Yanek, Lisa R., Zhou, Hufeng, Liu, Ching-Ti, North, Kari E., Justice, Anne E., Locke, Jonathan M., Owens, Nick, Murray, Anna, Patel, Kashyap, Frayling, Timothy M., Wright, Caroline F., Wood, Andrew R., Lin, Xihong, Manning, Alisa, and Weedon, Michael N.

Published
2024
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8. Publisher Correction: Understanding the genetic complexity of puberty timing across the allele frequency spectrum

Author

Kentistou, Katherine A., Kaisinger, Lena R., Stankovic, Stasa, Vaudel, Marc, Mendes de Oliveira, Edson, Messina, Andrea, Walters, Robin G., Liu, Xiaoxi, Busch, Alexander S., Helgason, Hannes, Thompson, Deborah J., Santoni, Federico, Petricek, Konstantin M., Zouaghi, Yassine, Huang-Doran, Isabel, Gudbjartsson, Daniel F., Bratland, Eirik, Lin, Kuang, Gardner, Eugene J., Zhao, Yajie, Jia, Raina Y., Terao, Chikashi, Riggan, Marjorie J., Bolla, Manjeet K., Yazdanpanah, Mojgan, Yazdanpanah, Nahid, Bradfield, Jonathan P., Broer, Linda, Campbell, Archie, Chasman, Daniel I., Cousminer, Diana L., Franceschini, Nora, Franke, Lude H., Girotto, Giorgia, He, Chunyan, Järvelin, Marjo-Riitta, Joshi, Peter K., Kamatani, Yoichiro, Karlsson, Robert, Luan, Jian’an, Lunetta, Kathryn L., Mägi, Reedik, Mangino, Massimo, Medland, Sarah E., Meisinger, Christa, Noordam, Raymond, Nutile, Teresa, Concas, Maria Pina, Polašek, Ozren, Porcu, Eleonora, Ring, Susan M., Sala, Cinzia, Smith, Albert V., Tanaka, Toshiko, van der Most, Peter J., Vitart, Veronique, Wang, Carol A., Willemsen, Gonneke, Zygmunt, Marek, Ahearn, Thomas U., Andrulis, Irene L., Anton-Culver, Hoda, Antoniou, Antonis C., Auer, Paul L., Barnes, Catriona L. K., Beckmann, Matthias W., Berrington de Gonzalez, Amy, Bogdanova, Natalia V., Bojesen, Stig E., Brenner, Hermann, Buring, Julie E., Canzian, Federico, Chang-Claude, Jenny, Couch, Fergus J., Cox, Angela, Crisponi, Laura, Czene, Kamila, Daly, Mary B., Demerath, Ellen W., Dennis, Joe, Devilee, Peter, De Vivo, Immaculata, Dörk, Thilo, Dunning, Alison M., Dwek, Miriam, Eriksson, Johan G., Fasching, Peter A., Fernandez-Rhodes, Lindsay, Ferreli, Liana, Fletcher, Olivia, Gago-Dominguez, Manuela, García-Closas, Montserrat, García-Sáenz, José A., González-Neira, Anna, Grallert, Harald, Guénel, Pascal, Haiman, Christopher A., Hall, Per, Hamann, Ute, Hakonarson, Hakon, Hart, Roger J., Hickey, Martha, Hooning, Maartje J., Hoppe, Reiner, Hopper, John L., Hottenga, Jouke-Jan, Hu, Frank B., Huebner, Hanna, Hunter, David J., Jernström, Helena, John, Esther M., Karasik, David, Khusnutdinova, Elza K., Kristensen, Vessela N., Lacey, James V., Lambrechts, Diether, Launer, Lenore J., Lind, Penelope A., Lindblom, Annika, Magnusson, Patrik K. E., Mannermaa, Arto, McCarthy, Mark I., Meitinger, Thomas, Menni, Cristina, Michailidou, Kyriaki, Millwood, Iona Y., Milne, Roger L., Montgomery, Grant W., Nevanlinna, Heli, Nolte, Ilja M., Nyholt, Dale R., Obi, Nadia, O’Brien, Katie M., Offit, Kenneth, Oldehinkel, Albertine J., Ostrowski, Sisse R., Palotie, Aarno, Pedersen, Ole B., Peters, Annette, Pianigiani, Giulia, Plaseska-Karanfilska, Dijana, Pouta, Anneli, Pozarickij, Alfred, Radice, Paolo, Rennert, Gad, Rosendaal, Frits R., Ruggiero, Daniela, Saloustros, Emmanouil, Sandler, Dale P., Schipf, Sabine, Schmidt, Carsten O., Schmidt, Marjanka K., Small, Kerrin, Spedicati, Beatrice, Stampfer, Meir, Stone, Jennifer, Tamimi, Rulla M., Teras, Lauren R., Tikkanen, Emmi, Turman, Constance, Vachon, Celine M., Wang, Qin, Winqvist, Robert, Wolk, Alicja, Zemel, Babette S., Zheng, Wei, van Dijk, Ko W., Alizadeh, Behrooz Z., Bandinelli, Stefania, Boerwinkle, Eric, Boomsma, Dorret I., Ciullo, Marina, Chenevix-Trench, Georgia, Cucca, Francesco, Esko, Tõnu, Gieger, Christian, Grant, Struan F. A., Gudnason, Vilmundur, Hayward, Caroline, Kolčić, Ivana, Kraft, Peter, Lawlor, Deborah A., Martin, Nicholas G., Nøhr, Ellen A., Pedersen, Nancy L., Pennell, Craig E., Ridker, Paul M., Robino, Antonietta, Snieder, Harold, Sovio, Ulla, Spector, Tim D., Stöckl, Doris, Sudlow, Cathie, Timpson, Nic J., Toniolo, Daniela, Uitterlinden, André, Ulivi, Sheila, Völzke, Henry, Wareham, Nicholas J., Widen, Elisabeth, Wilson, James F., Pharoah, Paul D. P., Li, Liming, Easton, Douglas F., Njølstad, Pål R., Sulem, Patrick, Murabito, Joanne M., Murray, Anna, Manousaki, Despoina, Juul, Anders, Erikstrup, Christian, Stefansson, Kari, Horikoshi, Momoko, Chen, Zhengming, Farooqi, I. Sadaf, Pitteloud, Nelly, Johansson, Stefan, Day, Felix R., Perry, John R. B., and Ong, Ken K.

Published
2024
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9. Deleterious heteroplasmic mitochondrial mutations are associated with an increased risk of overall and cancer-specific mortality

Author

Hong, Yun Soo, Battle, Stephanie L, Shi, Wen, Puiu, Daniela, Pillalamarri, Vamsee, Xie, Jiaqi, Pankratz, Nathan, Lake, Nicole J, Lek, Monkol, Rotter, Jerome I, Rich, Stephen S, Kooperberg, Charles, Reiner, Alex P, Auer, Paul L, Heard-Costa, Nancy, Liu, Chunyu, Lai, Meng, Murabito, Joanne M, Levy, Daniel, Grove, Megan L, Alonso, Alvaro, Gibbs, Richard, Dugan-Perez, Shannon, Gondek, Lukasz P, Guallar, Eliseo, and Arking, Dan E

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Genetics, Cancer, Human Genome, Hematology, 2.1 Biological and endogenous factors, Good Health and Well Being, Humans, Mitochondria, DNA, Mitochondrial, Heteroplasmy, Leukemia, Mutation
Abstract

Mitochondria carry their own circular genome and disruption of the mitochondrial genome is associated with various aging-related diseases. Unlike the nuclear genome, mitochondrial DNA (mtDNA) can be present at 1000 s to 10,000 s copies in somatic cells and variants may exist in a state of heteroplasmy, where only a fraction of the DNA molecules harbors a particular variant. We quantify mtDNA heteroplasmy in 194,871 participants in the UK Biobank and find that heteroplasmy is associated with a 1.5-fold increased risk of all-cause mortality. Additionally, we functionally characterize mtDNA single nucleotide variants (SNVs) using a constraint-based score, mitochondrial local constraint score sum (MSS) and find it associated with all-cause mortality, and with the prevalence and incidence of cancer and cancer-related mortality, particularly leukemia. These results indicate that mitochondria may have a functional role in certain cancers, and mitochondrial heteroplasmic SNVs may serve as a prognostic marker for cancer, especially for leukemia.

Published
2023

10. Clonal hematopoiesis of indeterminate potential, DNA methylation, and risk for coronary artery disease

Author

Uddin, MD Mesbah, Nguyen, Ngoc Quynh H, Yu, Bing, Brody, Jennifer A, Pampana, Akhil, Nakao, Tetsushi, Fornage, Myriam, Bressler, Jan, Sotoodehnia, Nona, Weinstock, Joshua S, Honigberg, Michael C, Nachun, Daniel, Bhattacharya, Romit, Griffin, Gabriel K, Chander, Varuna, Gibbs, Richard A, Rotter, Jerome I, Liu, Chunyu, Baccarelli, Andrea A, Chasman, Daniel I, Whitsel, Eric A, Kiel, Douglas P, Murabito, Joanne M, Boerwinkle, Eric, Ebert, Benjamin L, Jaiswal, Siddhartha, Floyd, James S, Bick, Alexander G, Ballantyne, Christie M, Psaty, Bruce M, Natarajan, Pradeep, and Conneely, Karen N

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Cardiovascular, Stem Cell Research - Nonembryonic - Non-Human, Aging, Heart Disease - Coronary Heart Disease, Atherosclerosis, Human Genome, Hematology, Stem Cell Research, Heart Disease, 2.1 Biological and endogenous factors, Aetiology, Cancer, Good Health and Well Being, Clonal Hematopoiesis, Coronary Artery Disease, DNA Methylation, Hematopoiesis, Hematopoietic Stem Cells, Humans
Abstract

Age-related changes to the genome-wide DNA methylation (DNAm) pattern observed in blood are well-documented. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by the age-related acquisition and expansion of leukemogenic mutations in hematopoietic stem cells (HSCs), is associated with blood cancer and coronary artery disease (CAD). Epigenetic regulators DNMT3A and TET2 are the two most frequently mutated CHIP genes. Here, we present results from an epigenome-wide association study for CHIP in 582 Cardiovascular Health Study (CHS) participants, with replication in 2655 Atherosclerosis Risk in Communities (ARIC) Study participants. We show that DNMT3A and TET2 CHIP have distinct and directionally opposing genome-wide DNAm association patterns consistent with their regulatory roles, albeit both promoting self-renewal of HSCs. Mendelian randomization analyses indicate that a subset of DNAm alterations associated with these two leading CHIP genes may promote the risk for CAD.

Published
2022

12. Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

Author

McCartney, Daniel L, Min, Josine L, Richmond, Rebecca C, Lu, Ake T, Sobczyk, Maria K, Davies, Gail, Broer, Linda, Guo, Xiuqing, Jeong, Ayoung, Jung, Jeesun, Kasela, Silva, Katrinli, Seyma, Kuo, Pei-Lun, Matias-Garcia, Pamela R, Mishra, Pashupati P, Nygaard, Marianne, Palviainen, Teemu, Patki, Amit, Raffield, Laura M, Ratliff, Scott M, Richardson, Tom G, Robinson, Oliver, Soerensen, Mette, Sun, Dianjianyi, Tsai, Pei-Chien, van der Zee, Matthijs D, Walker, Rosie M, Wang, Xiaochuan, Wang, Yunzhang, Xia, Rui, Xu, Zongli, Yao, Jie, Zhao, Wei, Correa, Adolfo, Boerwinkle, Eric, Dugué, Pierre-Antoine, Durda, Peter, Elliott, Hannah R, Gieger, Christian, de Geus, Eco JC, Harris, Sarah E, Hemani, Gibran, Imboden, Medea, Kähönen, Mika, Kardia, Sharon LR, Kresovich, Jacob K, Li, Shengxu, Lunetta, Kathryn L, Mangino, Massimo, Mason, Dan, McIntosh, Andrew M, Mengel-From, Jonas, Moore, Ann Zenobia, Murabito, Joanne M, Ollikainen, Miina, Pankow, James S, Pedersen, Nancy L, Peters, Annette, Polidoro, Silvia, Porteous, David J, Raitakari, Olli, Rich, Stephen S, Sandler, Dale P, Sillanpää, Elina, Smith, Alicia K, Southey, Melissa C, Strauch, Konstantin, Tiwari, Hemant, Tanaka, Toshiko, Tillin, Therese, Uitterlinden, Andre G, Van Den Berg, David J, van Dongen, Jenny, Wilson, James G, Wright, John, Yet, Idil, Arnett, Donna, Bandinelli, Stefania, Bell, Jordana T, Binder, Alexandra M, Boomsma, Dorret I, Chen, Wei, Christensen, Kaare, Conneely, Karen N, Elliott, Paul, Ferrucci, Luigi, Fornage, Myriam, Hägg, Sara, Hayward, Caroline, Irvin, Marguerite, Kaprio, Jaakko, Lawlor, Deborah A, Lehtimäki, Terho, Lohoff, Falk W, Milani, Lili, Milne, Roger L, Probst-Hensch, Nicole, Reiner, Alex P, Ritz, Beate, and Rotter, Jerome I

Subjects
Prevention, Nutrition, Aging, Human Genome, Genetics, Generic health relevance, Inflammatory and immune system, Good Health and Well Being, Adiposity, Biomarkers, C-Reactive Protein, CpG Islands, DNA Methylation, Educational Status, Epigenesis, Genetic, Genetic Loci, Genetic Markers, Genome, Human, Genome-Wide Association Study, Granulocytes, Humans, Immunity, Innate, Lipid Metabolism, Multifactorial Inheritance, Plasminogen Activator Inhibitor 1, DNA methylation, GWAS, Epigenetic clock, Genetics of DNA Methylation Consortium, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

BackgroundBiological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.ResultsLeveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels.ConclusionThis study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

Published
2021

13. Clonal hematopoiesis associated with epigenetic aging and clinical outcomes

Author

Nachun, Daniel, Lu, Ake T, Bick, Alexander G, Natarajan, Pradeep, Weinstock, Joshua, Szeto, Mindy D, Kathiresan, Sekar, Abecasis, Goncalo, Taylor, Kent D, Guo, Xiuqing, Tracy, Russ, Durda, Peter, Liu, Yongmei, Johnson, Craig, Rich, Stephen S, Van Den Berg, David, Laurie, Cecilia, Blackwell, Tom, Papanicolaou, George J, Correa, Adolfo, Raffield, Laura M, Johnson, Andrew D, Murabito, Joanne, Manson, JoAnn E, Desai, Pinkal, Kooperberg, Charles, Assimes, Themistocles L, Levy, Daniel, Rotter, Jerome I, Reiner, Alex P, Whitsel, Eric A, Wilson, James G, Horvath, Steve, Jaiswal, Siddhartha, and Consortium, the NHLBI Trans‐Omics for Precision Medicine

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Aging, Heart Disease, Cardiovascular, Patient Safety, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Clonal Hematopoiesis, Epigenomics, Humans, Risk Factors, Treatment Outcome, clonal hematopoiesis, epigenomics, heart disease, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Clonal hematopoiesis of indeterminate potential (CHIP) is a common precursor state for blood cancers that most frequently occurs due to mutations in the DNA-methylation modifying enzymes DNMT3A or TET2. We used DNA-methylation array and whole-genome sequencing data from four cohorts together comprising 5522 persons to study the association between CHIP, epigenetic clocks, and health outcomes. CHIP was strongly associated with epigenetic age acceleration, defined as the residual after regressing epigenetic clock age on chronological age, in several clocks, ranging from 1.31 years (GrimAge, p 0 in both Hannum and GrimAge (referred to as AgeAccelHG+). This group was at high risk of all-cause mortality (hazard ratio 2.90, p

Published
2021

17. Abstract 13362: Proteomic Architecture of Frailty Across the Spectrum of Cardiovascular Disease

Author

Perry, Andrew, Zhao, SHilin, Gajjar, Priya, Murthy, Venkatesh L, Lehallier, Benoit, Miller, Patricia, Nair, Sangeeta, Neill, Colin, Carr, Jeffrey, Fearon, William F, Kapadia, Samir R, Kumbhani, Dharam J, Gillam, Linda D, Lindenfeld, Joann, Farrell, Laurie, Marron, Megan M, Tian, Qu, Newman, Anne, Murabito, Joanne, Gerszten, Robert E, Nayor, Matthew G, Elmariah, Sammy, Lindman, Brian R, and Shah, Ravi V

Published
2023
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19. A meta-analysis of genome-wide association studies identifies multiple longevity genes.

Author

Deelen, Joris, Evans, Daniel S, Arking, Dan E, Tesi, Niccolò, Nygaard, Marianne, Liu, Xiaomin, Wojczynski, Mary K, Biggs, Mary L, van der Spek, Ashley, Atzmon, Gil, Ware, Erin B, Sarnowski, Chloé, Smith, Albert V, Seppälä, Ilkka, Cordell, Heather J, Dose, Janina, Amin, Najaf, Arnold, Alice M, Ayers, Kristin L, Barzilai, Nir, Becker, Elizabeth J, Beekman, Marian, Blanché, Hélène, Christensen, Kaare, Christiansen, Lene, Collerton, Joanna C, Cubaynes, Sarah, Cummings, Steven R, Davies, Karen, Debrabant, Birgit, Deleuze, Jean-François, Duncan, Rachel, Faul, Jessica D, Franceschi, Claudio, Galan, Pilar, Gudnason, Vilmundur, Harris, Tamara B, Huisman, Martijn, Hurme, Mikko A, Jagger, Carol, Jansen, Iris, Jylhä, Marja, Kähönen, Mika, Karasik, David, Kardia, Sharon LR, Kingston, Andrew, Kirkwood, Thomas BL, Launer, Lenore J, Lehtimäki, Terho, Lieb, Wolfgang, Lyytikäinen, Leo-Pekka, Martin-Ruiz, Carmen, Min, Junxia, Nebel, Almut, Newman, Anne B, Nie, Chao, Nohr, Ellen A, Orwoll, Eric S, Perls, Thomas T, Province, Michael A, Psaty, Bruce M, Raitakari, Olli T, Reinders, Marcel JT, Robine, Jean-Marie, Rotter, Jerome I, Sebastiani, Paola, Smith, Jennifer, Sørensen, Thorkild IA, Taylor, Kent D, Uitterlinden, André G, van der Flier, Wiesje, van der Lee, Sven J, van Duijn, Cornelia M, van Heemst, Diana, Vaupel, James W, Weir, David, Ye, Kenny, Zeng, Yi, Zheng, Wanlin, Holstege, Henne, Kiel, Douglas P, Lunetta, Kathryn L, Slagboom, P Eline, and Murabito, Joanne M

Subjects
Humans, Heat-Shock Proteins, Longevity, Apolipoprotein E2, Apolipoprotein E4, Genome-Wide Association Study
Abstract

Human longevity is heritable, but genome-wide association (GWA) studies have had limited success. Here, we perform two meta-analyses of GWA studies of a rigorous longevity phenotype definition including 11,262/3484 cases surviving at or beyond the age corresponding to the 90th/99th survival percentile, respectively, and 25,483 controls whose age at death or at last contact was at or below the age corresponding to the 60th survival percentile. Consistent with previous reports, rs429358 (apolipoprotein E (ApoE) ε4) is associated with lower odds of surviving to the 90th and 99th percentile age, while rs7412 (ApoE ε2) shows the opposite. Moreover, rs7676745, located near GPR78, associates with lower odds of surviving to the 90th percentile age. Gene-level association analysis reveals a role for tissue-specific expression of multiple genes in longevity. Finally, genetic correlation of the longevity GWA results with that of several disease-related phenotypes points to a shared genetic architecture between health and longevity.

Published
2019

20. Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors

Author

Warrington, Nicole M, Beaumont, Robin N, Horikoshi, Momoko, Day, Felix R, Helgeland, Øyvind, Laurin, Charles, Bacelis, Jonas, Peng, Shouneng, Hao, Ke, Feenstra, Bjarke, Wood, Andrew R, Mahajan, Anubha, Tyrrell, Jessica, Robertson, Neil R, Rayner, N William, Qiao, Zhen, Moen, Gunn-Helen, Vaudel, Marc, Marsit, Carmen J, Chen, Jia, Nodzenski, Michael, Schnurr, Theresia M, Zafarmand, Mohammad H, Bradfield, Jonathan P, Grarup, Niels, Kooijman, Marjolein N, Li-Gao, Ruifang, Geller, Frank, Ahluwalia, Tarunveer S, Paternoster, Lavinia, Rueedi, Rico, Huikari, Ville, Hottenga, Jouke-Jan, Lyytikäinen, Leo-Pekka, Cavadino, Alana, Metrustry, Sarah, Cousminer, Diana L, Wu, Ying, Thiering, Elisabeth, Wang, Carol A, Have, Christian T, Vilor-Tejedor, Natalia, Joshi, Peter K, Painter, Jodie N, Ntalla, Ioanna, Myhre, Ronny, Pitkänen, Niina, van Leeuwen, Elisabeth M, Joro, Raimo, Lagou, Vasiliki, Richmond, Rebecca C, Espinosa, Ana, Barton, Sheila J, Inskip, Hazel M, Holloway, John W, Santa-Marina, Loreto, Estivill, Xavier, Ang, Wei, Marsh, Julie A, Reichetzeder, Christoph, Marullo, Letizia, Hocher, Berthold, Lunetta, Kathryn L, Murabito, Joanne M, Relton, Caroline L, Kogevinas, Manolis, Chatzi, Leda, Allard, Catherine, Bouchard, Luigi, Hivert, Marie-France, Zhang, Ge, Muglia, Louis J, Heikkinen, Jani, Morgen, Camilla S, van Kampen, Antoine HC, van Schaik, Barbera DC, Mentch, Frank D, Langenberg, Claudia, Luan, Jian’an, Scott, Robert A, Zhao, Jing Hua, Hemani, Gibran, Ring, Susan M, Bennett, Amanda J, Gaulton, Kyle J, Fernandez-Tajes, Juan, van Zuydam, Natalie R, Medina-Gomez, Carolina, de Haan, Hugoline G, Rosendaal, Frits R, Kutalik, Zoltán, Marques-Vidal, Pedro, Das, Shikta, Willemsen, Gonneke, Mbarek, Hamdi, Müller-Nurasyid, Martina, Standl, Marie, Appel, Emil VR, Fonvig, Cilius E, and Trier, Caecilie

Subjects
Biological Sciences, Genetics, Cardiovascular, Perinatal Period - Conditions Originating in Perinatal Period, Nutrition, Prevention, Preterm, Low Birth Weight and Health of the Newborn, Pediatric, Obesity, Conditions Affecting the Embryonic and Fetal Periods, Infant Mortality, 2.1 Biological and endogenous factors, Aetiology, Reproductive health and childbirth, Good Health and Well Being, Adult, Birth Weight, Blood Pressure, Body Height, Diabetes Mellitus, Type 2, Female, Fetal Development, Genetic Predisposition to Disease, Genome-Wide Association Study, Heart Diseases, Humans, Infant, Newborn, Male, Maternal Inheritance, Maternal-Fetal Exchange, Metabolic Diseases, Models, Genetic, Polymorphism, Single Nucleotide, Pregnancy, Risk Factors, EGG Consortium, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.

Published
2019

22. Predictors of incident diabetes in two populations: framingham heart study and hispanic community health study / study of latinos

Author

Kaplan, Robert C., Song, Rebecca J., Lin, Juan, Xanthakis, Vanessa, Hua, Simin, Chernofsky, Ariel, Evenson, Kelly R., Walker, Maura E., Cuthbertson, Carmen, Murabito, Joanne M., Cordero, Christina, Daviglus, Martha, Perreira, Krista M., Gellman, Marc, Sotres-Alvarez, Daniela, Vasan, Ramachandran S., Xue, Xiaonan, Spartano, Nicole L., and Mossavar-Rahmani, Yasmin

Published
2022
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25. GWAS of epigenetic aging rates in blood reveals a critical role for TERT.

Author

Lu, Ake T, Xue, Luting, Salfati, Elias L, Chen, Brian H, Ferrucci, Luigi, Levy, Daniel, Joehanes, Roby, Murabito, Joanne M, Kiel, Douglas P, Tsai, Pei-Chien, Yet, Idil, Bell, Jordana T, Mangino, Massimo, Tanaka, Toshiko, McRae, Allan F, Marioni, Riccardo E, Visscher, Peter M, Wray, Naomi R, Deary, Ian J, Levine, Morgan E, Quach, Austin, Assimes, Themistocles, Tsao, Philip S, Absher, Devin, Stewart, James D, Li, Yun, Reiner, Alex P, Hou, Lifang, Baccarelli, Andrea A, Whitsel, Eric A, Aviv, Abraham, Cardona, Alexia, Day, Felix R, Wareham, Nicholas J, Perry, John RB, Ong, Ken K, Raj, Kenneth, Lunetta, Kathryn L, and Horvath, Steve

Subjects
Leukocytes, Cells, Cultured, Telomere, Fibroblasts, Humans, Telomerase, DNA Methylation, Epigenesis, Genetic, CpG Islands, Aging, Menopause, Menarche, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Child, Female, Male, Genome-Wide Association Study, Young Adult, Mendelian Randomization Analysis, Cells, Cultured, Epigenesis, Genetic, and over
Abstract

DNA methylation age is an accurate biomarker of chronological age and predicts lifespan, but its underlying molecular mechanisms are unknown. In this genome-wide association study of 9907 individuals, we find gene variants mapping to five loci associated with intrinsic epigenetic age acceleration (IEAA) and gene variants in three loci associated with extrinsic epigenetic age acceleration (EEAA). Mendelian randomization analysis suggests causal influences of menarche and menopause on IEAA and lipoproteins on IEAA and EEAA. Variants associated with longer leukocyte telomere length (LTL) in the telomerase reverse transcriptase gene (TERT) paradoxically confer higher IEAA (P

Published
2018

27. Association of Arterial Stiffness With Mid- to Long-Term Home Blood Pressure Variability in the Electronic Framingham Heart Study: Cohort Study

Author

Wang, Xuzhi, primary, Zhang, Yuankai, additional, Pathiravasan, Chathurangi H, additional, Ukonu, Nene C, additional, Rong, Jian, additional, Benjamin, Emelia J, additional, McManus, David D, additional, Larson, Martin G, additional, Vasan, Ramachandran S, additional, Hamburg, Naomi M, additional, Murabito, Joanne M, additional, Liu, Chunyu, additional, and Mitchell, Gary F, additional

Published
2024
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29. Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness.

Author

Willems, Sara M, Wright, Daniel J, Day, Felix R, Trajanoska, Katerina, Joshi, Peter K, Morris, John A, Matteini, Amy M, Garton, Fleur C, Grarup, Niels, Oskolkov, Nikolay, Thalamuthu, Anbupalam, Mangino, Massimo, Liu, Jun, Demirkan, Ayse, Lek, Monkol, Xu, Liwen, Wang, Guan, Oldmeadow, Christopher, Gaulton, Kyle J, Lotta, Luca A, Miyamoto-Mikami, Eri, Rivas, Manuel A, White, Tom, Loh, Po-Ru, Aadahl, Mette, Amin, Najaf, Attia, John R, Austin, Krista, Benyamin, Beben, Brage, Søren, Cheng, Yu-Ching, Cięszczyk, Paweł, Derave, Wim, Eriksson, Karl-Fredrik, Eynon, Nir, Linneberg, Allan, Lucia, Alejandro, Massidda, Myosotis, Mitchell, Braxton D, Miyachi, Motohiko, Murakami, Haruka, Padmanabhan, Sandosh, Pandey, Ashutosh, Papadimitriou, Ioannis, Rajpal, Deepak K, Sale, Craig, Schnurr, Theresia M, Sessa, Francesco, Shrine, Nick, Tobin, Martin D, Varley, Ian, Wain, Louise V, Wray, Naomi R, Lindgren, Cecilia M, MacArthur, Daniel G, Waterworth, Dawn M, McCarthy, Mark I, Pedersen, Oluf, Khaw, Kay-Tee, Kiel, Douglas P, GEFOS Any-Type of Fracture Consortium, Pitsiladis, Yannis, Fuku, Noriyuki, Franks, Paul W, North, Kathryn N, van Duijn, Cornelia M, Mather, Karen A, Hansen, Torben, Hansson, Ola, Spector, Tim, Murabito, Joanne M, Richards, J Brent, Rivadeneira, Fernando, Langenberg, Claudia, Perry, John RB, Wareham, Nick J, and Scott, Robert A

Subjects
GEFOS Any-Type of Fracture Consortium, Hand, Humans, Actins, Transforming Growth Factor alpha, Membrane Proteins, Neoplasm Proteins, Nuclear Proteins, Repressor Proteins, Hand Strength, Cohort Studies, Genetics, Population, Polymorphism, Single Nucleotide, Adult, Aged, Middle Aged, European Continental Ancestry Group, Female, Male, Genome-Wide Association Study, Genetic Loci, United Kingdom, Prevention, Genetics, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Musculoskeletal
Abstract

Hand grip strength is a widely used proxy of muscular fitness, a marker of frailty, and predictor of a range of morbidities and all-cause mortality. To investigate the genetic determinants of variation in grip strength, we perform a large-scale genetic discovery analysis in a combined sample of 195,180 individuals and identify 16 loci associated with grip strength (P

Published
2017

34. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program

Author

Taliun, Daniel, Harris, Daniel N., Kessler, Michael D., Carlson, Jedidiah, Szpiech, Zachary A., Torres, Raul, Taliun, Sarah A. Gagliano, Corvelo, André, Gogarten, Stephanie M., Kang, Hyun Min, Pitsillides, Achilleas N., LeFaive, Jonathon, Lee, Seung-been, Tian, Xiaowen, Browning, Brian L., Das, Sayantan, Emde, Anne-Katrin, Clarke, Wayne E., Loesch, Douglas P., Shetty, Amol C., Blackwell, Thomas W., Smith, Albert V., Wong, Quenna, Liu, Xiaoming, Conomos, Matthew P., Bobo, Dean M., Aguet, François, Albert, Christine, Alonso, Alvaro, Ardlie, Kristin G., Arking, Dan E., Aslibekyan, Stella, Auer, Paul L., Barnard, John, Barr, R. Graham, Barwick, Lucas, Becker, Lewis C., Beer, Rebecca L., Benjamin, Emelia J., Bielak, Lawrence F., Blangero, John, Boehnke, Michael, Bowden, Donald W., Brody, Jennifer A., Burchard, Esteban G., Cade, Brian E., Casella, James F., Chalazan, Brandon, Chasman, Daniel I., Chen, Yii-Der Ida, Cho, Michael H., Choi, Seung Hoan, Chung, Mina K., Clish, Clary B., Correa, Adolfo, Curran, Joanne E., Custer, Brian, Darbar, Dawood, Daya, Michelle, de Andrade, Mariza, DeMeo, Dawn L., Dutcher, Susan K., Ellinor, Patrick T., Emery, Leslie S., Eng, Celeste, Fatkin, Diane, Fingerlin, Tasha, Forer, Lukas, Fornage, Myriam, Franceschini, Nora, Fuchsberger, Christian, Fullerton, Stephanie M., Germer, Soren, Gladwin, Mark T., Gottlieb, Daniel J., Guo, Xiuqing, Hall, Michael E., He, Jiang, Heard-Costa, Nancy L., Heckbert, Susan R., Irvin, Marguerite R., Johnsen, Jill M., Johnson, Andrew D., Kaplan, Robert, Kardia, Sharon L. R., Kelly, Tanika, Kelly, Shannon, Kenny, Eimear E., Kiel, Douglas P., Klemmer, Robert, Konkle, Barbara A., Kooperberg, Charles, Köttgen, Anna, Lange, Leslie A., Lasky-Su, Jessica, Levy, Daniel, Lin, Xihong, Lin, Keng-Han, Liu, Chunyu, Loos, Ruth J. F., Garman, Lori, Gerszten, Robert, Lubitz, Steven A., Lunetta, Kathryn L., Mak, Angel C. Y., Manichaikul, Ani, Manning, Alisa K., Mathias, Rasika A., McManus, David D., McGarvey, Stephen T., Meigs, James B., Meyers, Deborah A., Mikulla, Julie L., Minear, Mollie A., Mitchell, Braxton D., Mohanty, Sanghamitra, Montasser, May E., Montgomery, Courtney, Morrison, Alanna C., Murabito, Joanne M., Natale, Andrea, Natarajan, Pradeep, Nelson, Sarah C., North, Kari E., O’Connell, Jeffrey R., Palmer, Nicholette D., Pankratz, Nathan, Peloso, Gina M., Peyser, Patricia A., Pleiness, Jacob, Post, Wendy S., Psaty, Bruce M., Rao, D. C., Redline, Susan, Reiner, Alexander P., Roden, Dan, Rotter, Jerome I., Ruczinski, Ingo, Sarnowski, Chloé, Schoenherr, Sebastian, Schwartz, David A., Seo, Jeong-Sun, Seshadri, Sudha, Sheehan, Vivien A., Sheu, Wayne H., Shoemaker, M. Benjamin, Smith, Nicholas L., Smith, Jennifer A., Sotoodehnia, Nona, Stilp, Adrienne M., Tang, Weihong, Taylor, Kent D., Telen, Marilyn, Thornton, Timothy A., Tracy, Russell P., Van Den Berg, David J., Vasan, Ramachandran S., Viaud-Martinez, Karine A., Vrieze, Scott, Weeks, Daniel E., Weir, Bruce S., Weiss, Scott T., Weng, Lu-Chen, Willer, Cristen J., Zhang, Yingze, Zhao, Xutong, Arnett, Donna K., Ashley-Koch, Allison E., Barnes, Kathleen C., Boerwinkle, Eric, Gabriel, Stacey, Gibbs, Richard, Rice, Kenneth M., Rich, Stephen S., Silverman, Edwin K., Qasba, Pankaj, Gan, Weiniu, Papanicolaou, George J., Nickerson, Deborah A., Browning, Sharon R., Zody, Michael C., Zöllner, Sebastian, Wilson, James G., Cupples, L. Adrienne, Laurie, Cathy C., Jaquish, Cashell E., Hernandez, Ryan D., O’Connor, Timothy D., and Abecasis, Gonçalo R.

Published
2021
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35. GWAS analysis of handgrip and lower body strength in older adults in the CHARGE consortium.

Author

Matteini, Amy M, Tanaka, Toshiko, Karasik, David, Atzmon, Gil, Chou, Wen-Chi, Eicher, John D, Johnson, Andrew D, Arnold, Alice M, Callisaya, Michele L, Davies, Gail, Evans, Daniel S, Holtfreter, Birte, Lohman, Kurt, Lunetta, Kathryn L, Mangino, Massimo, Smith, Albert V, Smith, Jennifer A, Teumer, Alexander, Yu, Lei, Arking, Dan E, Buchman, Aron S, Chibinik, Lori B, De Jager, Philip L, Evans, Denis A, Faul, Jessica D, Garcia, Melissa E, Gillham-Nasenya, Irina, Gudnason, Vilmundur, Hofman, Albert, Hsu, Yi-Hsiang, Ittermann, Till, Lahousse, Lies, Liewald, David C, Liu, Yongmei, Lopez, Lorna, Rivadeneira, Fernando, Rotter, Jerome I, Siggeirsdottir, Kristin, Starr, John M, Thomson, Russell, Tranah, Gregory J, Uitterlinden, André G, Völker, Uwe, Völzke, Henry, Weir, David R, Yaffe, Kristine, Zhao, Wei, Zhuang, Wei Vivian, Zmuda, Joseph M, Bennett, David A, Cummings, Steven R, Deary, Ian J, Ferrucci, Luigi, Harris, Tamara B, Kardia, Sharon LR, Kocher, Thomas, Kritchevsky, Stephen B, Psaty, Bruce M, Seshadri, Sudha, Spector, Timothy D, Srikanth, Velandai K, Windham, B Gwen, Zillikens, M Carola, Newman, Anne B, Walston, Jeremy D, Kiel, Douglas P, and Murabito, Joanne M

Subjects
Humans, Hand Strength, Cohort Studies, Reproducibility of Results, Chromatin Immunoprecipitation, Epigenesis, Genetic, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Adult, Aged, Muscle Strength, Genome-Wide Association Study, Molecular Sequence Annotation, SNP, aging, genomewide association, meta-analysis, muscle strength, older adults, Epigenesis, Genetic, Polymorphism, Single Nucleotide, Developmental Biology, Medical and Health Sciences, Biological Sciences
Abstract

Decline in muscle strength with aging is an important predictor of health trajectory in the elderly. Several factors, including genetics, are proposed contributors to variability in muscle strength. To identify genetic contributors to muscle strength, a meta-analysis of genomewide association studies of handgrip was conducted. Grip strength was measured using a handheld dynamometer in 27 581 individuals of European descent over 65 years of age from 14 cohort studies. Genomewide association analysis was conducted on ~2.7 million imputed and genotyped variants (SNPs). Replication of the most significant findings was conducted using data from 6393 individuals from three cohorts. GWAS of lower body strength was also characterized in a subset of cohorts. Two genomewide significant (P-value< 5 × 10(-8) ) and 39 suggestive (P-value< 5 × 10(-5) ) associations were observed from meta-analysis of the discovery cohorts. After meta-analysis with replication cohorts, genomewide significant association was observed for rs752045 on chromosome 8 (β = 0.47, SE = 0.08, P-value = 5.20 × 10(-10) ). This SNP is mapped to an intergenic region and is located within an accessible chromatin region (DNase hypersensitivity site) in skeletal muscle myotubes differentiated from the human skeletal muscle myoblasts cell line. This locus alters a binding motif of the CCAAT/enhancer-binding protein-β (CEBPB) that is implicated in muscle repair mechanisms. GWAS of lower body strength did not yield significant results. A common genetic variant in a chromosomal region that regulates myotube differentiation and muscle repair may contribute to variability in grip strength in the elderly. Further studies are needed to uncover the mechanisms that link this genetic variant with muscle strength.

Published
2016

36. DNA methylation-based measures of biological age: meta-analysis predicting time to death

Author

Chen, Brian H, Marioni, Riccardo E, Colicino, Elena, Peters, Marjolein J, Ward-Caviness, Cavin K, Tsai, Pei-Chien, Roetker, Nicholas S, Just, Allan C, Demerath, Ellen W, Guan, Weihua, Bressler, Jan, Fornage, Myriam, Studenski, Stephanie, Vandiver, Amy R, Moore, Ann Zenobia, Tanaka, Toshiko, Kiel, Douglas P, Liang, Liming, Vokonas, Pantel, Schwartz, Joel, Lunetta, Kathryn L, Murabito, Joanne M, Bandinelli, Stefania, Hernandez, Dena G, Melzer, David, Nalls, Michael, Pilling, Luke C, Price, Timothy R, Singleton, Andrew B, Gieger, Christian, Holle, Rolf, Kretschmer, Anja, Kronenberg, Florian, Kunze, Sonja, Linseisen, Jakob, Meisinger, Christine, Rathmann, Wolfgang, Waldenberger, Melanie, Visscher, Peter M, Shah, Sonia, Wray, Naomi R, McRae, Allan F, Franco, Oscar H, Hofman, Albert, Uitterlinden, André G, Absher, Devin, Assimes, Themistocles, Levine, Morgan E, Lu, Ake T, Tsao, Philip S, Hou, Lifang, Manson, JoAnn E, Carty, Cara L, LaCroix, Andrea Z, Reiner, Alexander P, Spector, Tim D, Feinberg, Andrew P, Levy, Daniel, Baccarelli, Andrea, van Meurs, Joyce, Bell, Jordana T, Peters, Annette, Deary, Ian J, Pankow, James S, Ferrucci, Luigi, and Horvath, Steve

Subjects
Biological Sciences, Genetics, Clinical Research, Prevention, Good Health and Well Being, Aging, DNA Methylation, Epigenesis, Genetic, Female, Humans, Logistic Models, Male, Mortality, Racial Groups, Risk Factors, Survival Analysis, T-Lymphocyte Subsets, all-cause mortality, lifespan, epigenetics, epigenetic clock, DNA methylation, mortality, Biochemistry and cell biology, Clinical sciences
Abstract

Estimates of biological age based on DNA methylation patterns, often referred to as "epigenetic age", "DNAm age", have been shown to be robust biomarkers of age in humans. We previously demonstrated that independent of chronological age, epigenetic age assessed in blood predicted all-cause mortality in four human cohorts. Here, we expanded our original observation to 13 different cohorts for a total sample size of 13,089 individuals, including three racial/ethnic groups. In addition, we examined whether incorporating information on blood cell composition into the epigenetic age metrics improves their predictive power for mortality. All considered measures of epigenetic age acceleration were predictive of mortality (p≤8.2x10-9), independent of chronological age, even after adjusting for additional risk factors (p

Published
2016

37. Interarm differences in systolic blood pressure and the risk of dementia and subclinical brain injury

Author

Pase, Matthew P, Beiser, Alexa, Aparicio, Hugo, DeCarli, Charles, Vasan, Ramachandran S, Murabito, Joanne, and Seshadri, Sudha

Subjects
Neurodegenerative, Neurosciences, Acquired Cognitive Impairment, Dementia, Brain Disorders, Alzheimer's Disease, Aging, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Cardiovascular, Neurological, Aged, Apolipoprotein E4, Blood Pressure, Brain, Brain Injuries, Cohort Studies, Female, Follow-Up Studies, Heterozygote, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Organ Size, Regression Analysis, Risk, Risk Factors, Alzheimer's disease, Blood pressure, Interarm differences in systolic blood pressure, Cerebrovascular disease, Atherosclerosis, Peripheral vascular disease, Ankle-brachial index, ABI, Magnetic resonance imaging, Framingham Heart Study, Clinical Sciences, Geriatrics
Abstract

IntroductionThis study examined whether interarm differences in systolic blood pressure (IDSBP) ≥10 mm Hg were associated with the risk of incident dementia and subclinical brain injury.MethodsBetween 1992 and 1998, 2063 participants of the Framingham Heart Study underwent assessment of IDSBP with results related to the 10-year risk of incident dementia including clinically characterized Alzheimer's disease. Secondary outcomes included markers of subclinical brain injury on magnetic resonance imaging.ResultsHigh IDSBP were associated with a greater risk of incident dementia (hazard ratio [HR] 1.92; 95% confidence interval [CI], 1.09-3.40) and Alzheimer's disease (HR, 2.32; 95% CI, 1.29-4.18), but only in those who carried an apolipoprotein E (APOE) ε4 allele. IDSBP also predicted lower total brain volumes and more prevalent silent brain infarcts in those who were APOE ε4 positive.DiscussionHigh IDSBP were associated with an increased risk of dementia, including clinical Alzheimer's disease, and subclinical brain injury in those who were APOE ε4 positive.

Published
2016

47. Sex hormone binding globulin and total testosterone levels are associated with in vivo tau burden differentially between the sexes: findings from the Framingham Study

Author

Buckley, Rachel F., primary, McGrath, Emer R, additional, Scott, Matthew R, additional, Coughlan, Gillian T, additional, Boyle, Rory, additional, Seto, Mabel, additional, Ghosh, Saptaparni, additional, Jacobs, Heidi I.L., additional, Satizabal, Claudia L., additional, Thibault, Emma G., additional, Vasan, Ramachandran S, additional, Murabito, Joanne M, additional, Sperling, Reisa A., additional, Johnson, Keith A., additional, Seshadri, Sudha, additional, Jasuja, Ravi, additional, Bhasin, Shalender, additional, and Beiser, Alexa S., additional

Published
2023
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48. DNA methylation age of blood predicts all-cause mortality in later life

Author

Marioni, Riccardo E, Shah, Sonia, McRae, Allan F, Chen, Brian H, Colicino, Elena, Harris, Sarah E, Gibson, Jude, Henders, Anjali K, Redmond, Paul, Cox, Simon R, Pattie, Alison, Corley, Janie, Murphy, Lee, Martin, Nicholas G, Montgomery, Grant W, Feinberg, Andrew P, Fallin, M Daniele, Multhaup, Michael L, Jaffe, Andrew E, Joehanes, Roby, Schwartz, Joel, Just, Allan C, Lunetta, Kathryn L, Murabito, Joanne M, Starr, John M, Horvath, Steve, Baccarelli, Andrea A, Levy, Daniel, Visscher, Peter M, Wray, Naomi R, and Deary, Ian J

Subjects
Genetics, Prevention, Aging, Cardiovascular, Heart Disease, Clinical Research, Human Genome, 4.1 Discovery and preclinical testing of markers and technologies, Detection, screening and diagnosis, Good Health and Well Being, Aged, Blood, Cause of Death, Cohort Studies, DNA Methylation, Demography, Female, Humans, Inheritance Patterns, Leukocyte Count, Male, Mortality, Risk Factors, Survival Analysis, Environmental Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract

BackgroundDNA methylation levels change with age. Recent studies have identified biomarkers of chronological age based on DNA methylation levels. It is not yet known whether DNA methylation age captures aspects of biological age.ResultsHere we test whether differences between people's chronological ages and estimated ages, DNA methylation age, predict all-cause mortality in later life. The difference between DNA methylation age and chronological age (Δage) was calculated in four longitudinal cohorts of older people. Meta-analysis of proportional hazards models from the four cohorts was used to determine the association between Δage and mortality. A 5-year higher Δage is associated with a 21% higher mortality risk, adjusting for age and sex. After further adjustments for childhood IQ, education, social class, hypertension, diabetes, cardiovascular disease, and APOE e4 status, there is a 16% increased mortality risk for those with a 5-year higher Δage. A pedigree-based heritability analysis of Δage was conducted in a separate cohort. The heritability of Δage was 0.43.ConclusionsDNA methylation-derived measures of accelerated aging are heritable traits that predict mortality independently of health status, lifestyle factors, and known genetic factors.

Published
2015

50. Genome-wide meta-analysis of muscle weakness identifies 15 susceptibility loci in older men and women

Author

Jones, Garan, Trajanoska, Katerina, Santanasto, Adam J., Stringa, Najada, Kuo, Chia-Ling, Atkins, Janice L., Lewis, Joshua R., Duong, ThuyVy, Hong, Shengjun, Biggs, Mary L., Luan, Jian’an, Sarnowski, Chloe, Lunetta, Kathryn L., Tanaka, Toshiko, Wojczynski, Mary K., Cvejkus, Ryan, Nethander, Maria, Ghasemi, Sahar, Yang, Jingyun, Zillikens, M. Carola, Walter, Stefan, Sicinski, Kamil, Kague, Erika, Ackert-Bicknell, Cheryl L., Arking, Dan E., Windham, B. Gwen, Boerwinkle, Eric, Grove, Megan L., Graff, Misa, Spira, Dominik, Demuth, Ilja, van der Velde, Nathalie, de Groot, Lisette C. P. G. M., Psaty, Bruce M., Odden, Michelle C., Fohner, Alison E., Langenberg, Claudia, Wareham, Nicholas J., Bandinelli, Stefania, van Schoor, Natasja M., Huisman, Martijn, Tan, Qihua, Zmuda, Joseph, Mellström, Dan, Karlsson, Magnus, Bennett, David A., Buchman, Aron S., De Jager, Philip L., Uitterlinden, Andre G., Völker, Uwe, Kocher, Thomas, Teumer, Alexander, Rodriguéz-Mañas, Leocadio, García, Francisco J., Carnicero, José A., Herd, Pamela, Bertram, Lars, Ohlsson, Claes, Murabito, Joanne M., Melzer, David, Kuchel, George A., Ferrucci, Luigi, Karasik, David, Rivadeneira, Fernando, Kiel, Douglas P., and Pilling, Luke C.

Published
2021
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