Author: "Chaffin, Mark" / Database: Supplemental Index - Searchworks@Jio Institute Digital Library Search Results

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51 results on '"Chaffin, Mark"'

1. Rare coding variant analysis for human diseases across biobanks and ancestries

Author: Jurgens, Sean J., Wang, Xin, Choi, Seung Hoan, Weng, Lu-Chen, Koyama, Satoshi, Pirruccello, James P., Nguyen, Trang, Smadbeck, Patrick, Jang, Dongkeun, Chaffin, Mark, Walsh, Roddy, Roselli, Carolina, Elliott, Amanda L., Wijdeveld, Leonoor F. J. M., Biddinger, Kiran J., Kany, Shinwan, Rämö, Joel T., Natarajan, Pradeep, Aragam, Krishna G., Flannick, Jason, Burtt, Noël P., Bezzina, Connie R., Lubitz, Steven A., Lunetta, Kathryn L., and Ellinor, Patrick T.
Abstract: Large-scale sequencing has enabled unparalleled opportunities to investigate the role of rare coding variation in human phenotypic variability. Here, we present a pan-ancestry analysis of sequencing data from three large biobanks, including the All of Us research program. Using mixed-effects models, we performed gene-based rare variant testing for 601 diseases across 748,879 individuals, including 155,236 with ancestry dissimilar to European. We identified 363 significant associations, which highlighted core genes for the human disease phenome and identified potential novel associations, including UBR3for cardiometabolic disease and YLPM1for psychiatric disease. Pan-ancestry burden testing represented an inclusive and useful approach for discovery in diverse datasets, although we also highlight the importance of ancestry-specific sensitivity analyses in this setting. Finally, we found that effect sizes for rare protein-disrupting variants were concordant between samples similar to European ancestry and other genetic ancestries (βDeming= 0.7–1.0). Our results have implications for multi-ancestry and cross-biobank approaches in sequencing association studies for human disease.
Published: 2024
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2. Deep learning enables genetic analysis of the human thoracic aorta

Author: Pirruccello, James P., Chaffin, Mark D., Chou, Elizabeth L., Fleming, Stephen J., Lin, Honghuang, Nekoui, Mahan, Khurshid, Shaan, Friedman, Samuel F., Bick, Alexander G., Arduini, Alessandro, Weng, Lu-Chen, Choi, Seung Hoan, Akkad, Amer-Denis, Batra, Puneet, Tucker, Nathan R., Hall, Amelia W., Roselli, Carolina, Benjamin, Emelia J., Vellarikkal, Shamsudheen K., Gupta, Rajat M., Stegmann, Christian M., Juric, Dejan, Stone, James R., Vasan, Ramachandran S., Ho, Jennifer E., Hoffmann, Udo, Lubitz, Steven A., Philippakis, Anthony A., Lindsay, Mark E., and Ellinor, Patrick T.
Abstract: Enlargement or aneurysm of the aorta predisposes to dissection, an important cause of sudden death. We trained a deep learning model to evaluate the dimensions of the ascending and descending thoracic aorta in 4.6 million cardiac magnetic resonance images from the UK Biobank. We then conducted genome-wide association studies in 39,688 individuals, identifying 82 loci associated with ascending and 47 with descending thoracic aortic diameter, of which 14 loci overlapped. Transcriptome-wide analyses, rare-variant burden tests and human aortic single nucleus RNA sequencing prioritized genes including SVIL, which was strongly associated with descending aortic diameter. A polygenic score for ascending aortic diameter was associated with thoracic aortic aneurysm in 385,621 UK Biobank participants (hazard ratio = 1.43 per s.d., confidence interval 1.32–1.54, P= 3.3 × 10−20). Our results illustrate the potential for rapidly defining quantitative traits with deep learning, an approach that can be broadly applied to biomedical images.
Published: 2024
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3. Transfer learning enables predictions in network biology

Author: Theodoris, Christina V., Xiao, Ling, Chopra, Anant, Chaffin, Mark D., Al Sayed, Zeina R., Hill, Matthew C., Mantineo, Helene, Brydon, Elizabeth M., Zeng, Zexian, Liu, X. Shirley, and Ellinor, Patrick T.
Abstract: Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding1,2and computer vision3by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets.
Published: 2023
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4. Adjusting for common variant polygenic scores improves yield in rare variant association analyses

Author: Jurgens, Sean J., Pirruccello, James P., Choi, Seung Hoan, Morrill, Valerie N., Chaffin, Mark, Lubitz, Steven A., Lunetta, Kathryn L., and Ellinor, Patrick T.
Abstract: With the emergence of large-scale sequencing data, methods for improving power in rare variant association tests are needed. Here we show that adjusting for common variant polygenic scores improves yield in gene-based rare variant association tests across 65 quantitative traits in the UK Biobank (up to 20% increase at α= 2.6 × 10−6), without marked increases in false-positive rates or genomic inflation. Benefits were seen for various models, with the largest improvements seen for efficient sparse mixed-effects models. Our results illustrate how polygenic score adjustment can efficiently improve power in rare variant association discovery.
Published: 2023
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5. Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender

Author: Fleming, Stephen J., Chaffin, Mark D., Arduini, Alessandro, Akkad, Amer-Denis, Banks, Eric, Marioni, John C., Philippakis, Anthony A., Ellinor, Patrick T., and Babadi, Mehrtash
Abstract: Droplet-based single-cell assays, including single-cell RNA sequencing (scRNA-seq), single-nucleus RNA sequencing (snRNA-seq) and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), generate considerable background noise counts, the hallmark of which is nonzero counts in cell-free droplets and off-target gene expression in unexpected cell types. Such systematic background noise can lead to batch effects and spurious differential gene expression results. Here we develop a deep generative model based on the phenomenology of noise generation in droplet-based assays. The proposed model accurately distinguishes cell-containing droplets from cell-free droplets, learns the background noise profile and provides noise-free quantification in an end-to-end fashion. We implement this approach in the scalable and robust open-source software package CellBender. Analysis of simulated data demonstrates that CellBender operates near the theoretically optimal denoising limit. Extensive evaluations using real datasets and experimental benchmarks highlight enhanced concordance between droplet-based single-cell data and established gene expression patterns, while the learned background noise profile provides evidence of degraded or uncaptured cell types.
Published: 2023
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6. Aortic Cellular Diversity and Quantitative Genome-Wide Association Study Trait Prioritization Through Single-Nuclear RNA Sequencing of the Aneurysmal Human Aorta

Author: Chou, Elizabeth L., Chaffin, Mark, Simonson, Bridget, Pirruccello, James P., Akkad, Amer-Denis, Nekoui, Mahan, Lino Cardenas, Christian Lacks, Bedi, Kenneth C., Nash, Craig, Juric, Dejan, Stone, James R., Isselbacher, Eric M., Margulies, Kenneth B., Klattenhoff, Carla, Ellinor, Patrick T., and Lindsay, Mark E.
Published: 2022
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7. Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy

Author: Chaffin, Mark, Papangeli, Irinna, Simonson, Bridget, Akkad, Amer-Denis, Hill, Matthew C., Arduini, Alessandro, Fleming, Stephen J., Melanson, Michelle, Hayat, Sikander, Kost-Alimova, Maria, Atwa, Ondine, Ye, Jiangchuan, Bedi, Kenneth C., Nahrendorf, Matthias, Kaushik, Virendar K., Stegmann, Christian M., Margulies, Kenneth B., Tucker, Nathan R., and Ellinor, Patrick T.
Abstract: Heart failure encompasses a heterogeneous set of clinical features that converge on impaired cardiac contractile function1,2and presents a growing public health concern. Previous work has highlighted changes in both transcription and protein expression in failing hearts3,4, but may overlook molecular changes in less prevalent cell types. Here we identify extensive molecular alterations in failing hearts at single-cell resolution by performing single-nucleus RNA sequencing of nearly 600,000 nuclei in left ventricle samples from 11 hearts with dilated cardiomyopathy and 15 hearts with hypertrophic cardiomyopathy as well as 16 non-failing hearts. The transcriptional profiles of dilated or hypertrophic cardiomyopathy hearts broadly converged at the tissue and cell-type level. Further, a subset of hearts from patients with cardiomyopathy harbour a unique population of activated fibroblasts that is almost entirely absent from non-failing samples. We performed a CRISPR-knockout screen in primary human cardiac fibroblasts to evaluate this fibrotic cell state transition; knockout of genes associated with fibroblast transition resulted in a reduction of myofibroblast cell-state transition upon TGFβ1 stimulation for a subset of genes. Our results provide insights into the transcriptional diversity of the human heart in health and disease as well as new potential therapeutic targets and biomarkers for heart failure.
Published: 2022
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8. Genetic analysis of right heart structure and function in 40,000 people

Author: Pirruccello, James P., Di Achille, Paolo, Nauffal, Victor, Nekoui, Mahan, Friedman, Samuel F., Klarqvist, Marcus D. R., Chaffin, Mark D., Weng, Lu-Chen, Cunningham, Jonathan W., Khurshid, Shaan, Roselli, Carolina, Lin, Honghuang, Koyama, Satoshi, Ito, Kaoru, Kamatani, Yoichiro, Komuro, Issei, Jurgens, Sean J., Benjamin, Emelia J., Batra, Puneet, Natarajan, Pradeep, Ng, Kenney, Hoffmann, Udo, Lubitz, Steven A., Ho, Jennifer E., Lindsay, Mark E., Philippakis, Anthony A., and Ellinor, Patrick T.
Abstract: Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5, TBX5/TBX3, WNT9Band GATA4. A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P= 7.1 × 10−13) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function.
Published: 2022
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9. Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank

Author: Jurgens, Sean J., Choi, Seung Hoan, Morrill, Valerie N., Chaffin, Mark, Pirruccello, James P., Halford, Jennifer L., Weng, Lu-Chen, Nauffal, Victor, Roselli, Carolina, Hall, Amelia W., Oetjens, Matthew T., Lagerman, Braxton, vanMaanen, David P., Aragam, Krishna G., Lunetta, Kathryn L., Haggerty, Christopher M., Lubitz, Steven A., and Ellinor, Patrick T.
Abstract: Cardiometabolic diseases are the leading cause of death worldwide. Despite a known genetic component, our understanding of these diseases remains incomplete. Here, we analyzed the contribution of rare variants to 57 diseases and 26 cardiometabolic traits, using data from 200,337 UK Biobank participants with whole-exome sequencing. We identified 57 gene-based associations, with broad replication of novel signals in Geisinger MyCode. There was a striking risk associated with mutations in known Mendelian disease genes, including MYBPC3, LDLR, GCK, PKD1and TTN. Many genes showed independent convergence of rare and common variant evidence, including an association between GIGYF1and type 2 diabetes. We identified several large effect associations for height and 18 unique genes associated with blood lipid or glucose levels. Finally, we found that between 1.0% and 2.4% of participants carried rare potentially pathogenic variants for cardiometabolic disorders. These findings may facilitate studies aimed at therapeutics and screening of these common disorders.
Published: 2022
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10. Su1581 LOSS OF MTARC1 PROTECTS AGAINST METABOLIC DYSFUNCTIONASSOCIATED STEATOTIC LIVER DISEASE IN MICE.

Author: Bickerton, Caroline, Yin, Xiaofei, MacDonald, Bryan, Arduini, Alessandro, Haas, Mary, Chaffin, Mark, Deik, Amy, Clish, Clary B., Kovacs-Bogdan, Erika, Leed, Alison, Ehrmann, Alexander, and Ellinor, Patrick
Published: 2024
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11. Single-cell meta-analysis of SARS-CoV-2 entry genes across tissues and demographics

Author: Muus, Christoph, Luecken, Malte D., Eraslan, Gökcen, Sikkema, Lisa, Waghray, Avinash, Heimberg, Graham, Kobayashi, Yoshihiko, Vaishnav, Eeshit Dhaval, Subramanian, Ayshwarya, Smillie, Christopher, Jagadeesh, Karthik A., Duong, Elizabeth Thu, Fiskin, Evgenij, Triglia, Elena Torlai, Ansari, Meshal, Cai, Peiwen, Lin, Brian, Buchanan, Justin, Chen, Sijia, Shu, Jian, Haber, Adam L., Chung, Hattie, Montoro, Daniel T., Adams, Taylor, Aliee, Hananeh, Allon, Samuel J., Andrusivova, Zaneta, Angelidis, Ilias, Ashenberg, Orr, Bassler, Kevin, Bécavin, Christophe, Benhar, Inbal, Bergenstråhle, Joseph, Bergenstråhle, Ludvig, Bolt, Liam, Braun, Emelie, Bui, Linh T., Callori, Steven, Chaffin, Mark, Chichelnitskiy, Evgeny, Chiou, Joshua, Conlon, Thomas M., Cuoco, Michael S., Cuomo, Anna S. E., Deprez, Marie, Duclos, Grant, Fine, Denise, Fischer, David S., Ghazanfar, Shila, Gillich, Astrid, Giotti, Bruno, Gould, Joshua, Guo, Minzhe, Gutierrez, Austin J., Habermann, Arun C., Harvey, Tyler, He, Peng, Hou, Xiaomeng, Hu, Lijuan, Hu, Yan, Jaiswal, Alok, Ji, Lu, Jiang, Peiyong, Kapellos, Theodoros S., Kuo, Christin S., Larsson, Ludvig, Leney-Greene, Michael A., Lim, Kyungtae, Litviňuková, Monika, Ludwig, Leif S., Lukassen, Soeren, Luo, Wendy, Maatz, Henrike, Madissoon, Elo, Mamanova, Lira, Manakongtreecheep, Kasidet, Leroy, Sylvie, Mayr, Christoph H., Mbano, Ian M., McAdams, Alexi M., Nabhan, Ahmad N., Nyquist, Sarah K., Penland, Lolita, Poirion, Olivier B., Poli, Sergio, Qi, CanCan, Queen, Rachel, Reichart, Daniel, Rosas, Ivan, Schupp, Jonas C., Shea, Conor V., Shi, Xingyi, Sinha, Rahul, Sit, Rene V., Slowikowski, Kamil, Slyper, Michal, Smith, Neal P., Sountoulidis, Alex, Strunz, Maximilian, Sullivan, Travis B., Sun, Dawei, Talavera-López, Carlos, Tan, Peng, Tantivit, Jessica, Travaglini, Kyle J., Tucker, Nathan R., Vernon, Katherine A., Wadsworth, Marc H., Waldman, Julia, Wang, Xiuting, Xu, Ke, Yan, Wenjun, Zhao, William, and Ziegler, Carly G. K.
Abstract: Angiotensin-converting enzyme 2 (ACE2) and accessory proteases (TMPRSS2 and CTSL) are needed for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cellular entry, and their expression may shed light on viral tropism and impact across the body. We assessed the cell-type-specific expression of ACE2, TMPRSS2and CTSLacross 107 single-cell RNA-sequencing studies from different tissues. ACE2, TMPRSS2and CTSLare coexpressed in specific subsets of respiratory epithelial cells in the nasal passages, airways and alveoli, and in cells from other organs associated with coronavirus disease 2019 (COVID-19) transmission or pathology. We performed a meta-analysis of 31 lung single-cell RNA-sequencing studies with 1,320,896 cells from 377 nasal, airway and lung parenchyma samples from 228 individuals. This revealed cell-type-specific associations of age, sex and smoking with expression levels of ACE2, TMPRSS2and CTSL. Expression of entry factors increased with age and in males, including in airway secretory cells and alveolar type 2 cells. Expression programs shared by ACE2+TMPRSS2+cells in nasal, lung and gut tissues included genes that may mediate viral entry, key immune functions and epithelial–macrophage cross-talk, such as genes involved in the interleukin-6, interleukin-1, tumor necrosis factor and complement pathways. Cell-type-specific expression patterns may contribute to the pathogenesis of COVID-19, and our work highlights putative molecular pathways for therapeutic intervention.
Published: 2021
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12. Transcriptional and Cellular Diversity of the Human Heart

Author: Tucker, Nathan R., Chaffin, Mark, Fleming, Stephen J., Hall, Amelia W., Parsons, Victoria A., Bedi, Kenneth C., Akkad, Amer-Denis, Herndon, Caroline N., Arduini, Alessandro, Papangeli, Irinna, Roselli, Carolina, Aguet, François, Choi, Seung Hoan, Ardlie, Kristin G., Babadi, Mehrtash, Margulies, Kenneth B., Stegmann, Christian M., and Ellinor, Patrick T.
Abstract: Supplemental Digital Content is available in the text.
Published: 2020
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13. Whole Genome Sequencing Identifies CRISPLD2as a Lung Function Gene in Children With Asthma

Author: Kachroo, Priyadarshini, Hecker, Julian, Chawes, Bo L., Ahluwalia, Tarunveer S., Cho, Michael H., Qiao, Dandi, Kelly, Rachel S., Chu, Su H., Virkud, Yamini V., Huang, Mengna, Barnes, Kathleen C., Burchard, Esteban G., Eng, Celeste, Hu, Donglei, Celedón, Juan C., Daya, Michelle, Levin, Albert M., Gui, Hongsheng, Williams, L. Keoki, Forno, Erick, Mak, Angel C.Y., Avila, Lydiana, Soto-Quiros, Manuel E., Cloutier, Michelle M., Acosta-Pérez, Edna, Canino, Glorisa, Bønnelykke, Klaus, Bisgaard, Hans, Raby, Benjamin A., Lange, Christoph, Weiss, Scott T., Lasky-Su, Jessica A., Abe, Namiko, Abecasis, Goncalo, Albert, Christine, Palmer Allred, Nicholette (Nichole), Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Deborah, Arking, Dan, Arnett, Donna K., Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Barnard, John, Barnes, Kathleen, Barr, R. Graham, Barron-Casella, Emily, Beaty, Terri, Becker, Diane, Becker, Lewis, Beer, Rebecca, Begum, Ferdouse, Beitelshees, Amber, Benjamin, Emelia, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Boerwinkle, Eric, Borecki, Ingrid, Bowler, Russell, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Bunting, Karen, Burchard, Esteban, Cardwell, Jonathan, Carty, Cara, Casaburi, Richard, Casella, James, Chaffin, Mark, Chang, Christy, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Chen, Yii-Der Ida, Cho, Michael H., Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L. Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, Das, Sayantan, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Do, Ron, Duan, Qing, Duggirala, Ravi, Durda, Peter, Dutcher, Susan, Eaton, Charles, Ekunwe, Lynette, Ellinor, Patrick, Emery, Leslie, Farber, Charles, Farnam, Leanna, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Fu, Mao, Fullerton, Stephanie M., Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Yan, Gass, Margery, Gelb, Bruce, Geng, Xiaoqi (Priscilla), Germer, Soren, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Gu, C. Charles, Guan, Yue, Guo, Xiuqing, Haessler, Jeff, Hall, Michael, Harris, Daniel, Hawley, Nicola, He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hokanson, John, Holly, Kramer, Hong, Elliott, Hoth, Karin, (Agnes) Hsiung, Chao, Huston, Haley, Hwu, Chii Min, Irvin, Marguerite Ryan, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Jhun, Min A., Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kachroo, Priyadarshini, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kathiresan, Sekar, Kaufman, Laura, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Krauter, Stephanie, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Seunggeun Shawn, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Dan, Lewis, Joshua, Li, Yun, Lin, Honghuang, Lin, Keng Han, Liu, Simin, Liu, Yongmei, Loos, Ruth, Lubitz, Steven, Lunetta, Kathryn, Luo, James, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manson, JoAnn, Margolin, Lauren, Martin, Lisa, Mathai, Susan, Mathias, Rasika, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, Mei, Hao, Meyers, Deborah A., Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L., Mitchell, Braxton, Montasser, May E., Musani, Solomon, Mwasongwe, Stanford, Mychaleckyj, Josyf C., Nadkarni, Girish, Naik, Rakhi, Natarajan, Pradeep, Nekhai, Sergei, Nickerson, Deborah, North, Kari, O'Connell, Jeff, O'Connor, Tim, Ochs-Balcom, Heather, Pankow, James, Papanicolaou, George, Parker, Margaret, Parsa, Afshin, Penchev, Sara, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Lawrence S., Phillips, Sam, Pollin, Toni, Post, Wendy, Becker, Julia Powers, Boorgula, Meher Preethi, Preuss, Michael, Prokopenko, Dmitry, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Ramachandran, Vasan, Rao, D.C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Regan, Elizabeth, Reiner, Alex, Rice, Ken, Rich, Stephen, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sakornsakolpat, Phuwanat, Salimi, Shabnam, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay, Scheller, Christopher, Schmidt, Ellen, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Seidman, Jonathan, Sheehan, Vivien, Shetty, Amol, Shetty, Aniket, Sheu, Wayne Hui-Heng, Shoemaker, M. Benjamin, Silver, Brian, Silverman, Edwin, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne, Streeten, Elizabeth, Sung, Yun Ju, Su-Lasky, Jessica, Sylvia, Jody, Szpiro, Adam, Sztalryd, Carole, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Tinker, Lesley, Tirschwell, David, Tiwari, Hemant, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wan, Emily, Wang, Fei Fei, Watson, Karol, Weeks, Daniel E., Weir, Bruce, Weiss, Scott, Weng, Lu-Chen, Willer, Cristen, Williams, Kayleen, Williams, L. Keoki, Wilson, Carla, Wilson, James, Wong, Quenna, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yang, Rongze, Zaghloul, Norann, Zekavat, Maryam, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zheng, Xiuwen, Zhi, Degui, Zhou, Xiang, Zody, Michael, and Zoellner, Sebastian
Abstract: Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma.
Published: 2019
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14. Leveraging Human Genetics to Estimate Clinical Risk Reductions Achievable by Inhibiting Factor XI.

Author: Georgi, Benjamin, Mielke, Johanna, Chaffin, Mark, Khera, Amit V., Gelis, Lian, Mundl, Hardi, van Giezen, J.J.J., Ellinor, Patrick, Kathiresan, Sekar, Ziegelbauer, Karl, and Freitag, Daniel F.
Published: 2019
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15. MetProc: Separating Measurement Artifacts from True Metabolites in an Untargeted Metabolomics Experiment.

Author: Chaffin, Mark D., Cao, Liu, Deik, Amy A., Clish, Clary B., Hu, Frank B., Martínez-González, Miguel A., Razquin, Cristina, Bullo, Monica, Corella, Dolores, Gómez-Gracia, Enrique, Fiol, Miquel, Estruch, Ramon, Lapetra, José, Fitó, Montserrat, Arós, Fernando, Serra-Majem, Lluís, Ros, Emilio, and Liang, Liming
Published: 2019
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16. A genetic-association study of circulating coagulation factor VIII and von Willebrand factor levels

Author: de Vries, Paul S., Reventun, Paula, Brown, Michael R., Heath, Adam S., Huffman, Jennifer E., Le, Ngoc-Quynh, Bebo, Allison, Brody, Jennifer A., Temprano-Sagrera, Gerard, Raffield, Laura M., Ozel, Ayse Bilge, Thibord, Florian, Jain, Deepti, Lewis, Joshua P., Rodriguez, Benjmain A. T., Pankratz, Nathan, Taylor, Kent D., Polasek, Ozren, Chen, Ming-Huei, 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, Ekunwe, Lynette, Davies, Gail, Delgado, Graciela E., Suchon, Pierre, Guo, Xiuqing, Rosendaal, Frits R., Klaric, Lucija, Noordam, Raymond, van Rooij, Frank, Curran, Joanne E., Wheeler, Marsha M., Osburn, William O., O'Connell, Jeffrey R., Boerwinkle, Eric, Beswick, Andrew, Psaty, Bruce M., Kolcic, Ivana, Souto, Juan Carlos, Becker, Lewis C., Hansen, Torben, Doyle, Margaret F., Harris, Sarah E., Moissl, Angela P., Deleuze, Jean-François, Rich, Stephen S., van Hylckama Vlieg, Astrid, Campbell, Harry, Stott, David J., Soria, Jose Manuel, de Maat, Moniek P. M., Almasy, Laura, Brody, Lawrence C., Auer, Paul L., 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, 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, 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, Kooperberg, Charles, Hveem, Kristian, Ridker, Paul M., Morange, Pierre-Emmanuel, Johnson, Andrew D., Kabrhel, Christopher, AlexandreTrégouët, David, Smith, Nicholas L., Mitchell, Braxton D., Ben-Shlomo, Yoav, Fornage, Myriam, Hayward, Caroline, Mathias, Rasika A., Kilpeläinen, Tuomas O., Lange, Leslie A., Cox, Simon R., März, Winfried, Morange, Pierre-Emmanuel, Rotter, Jerome I., Mook-Kanamori, Dennis O., Wilson, James F., van der Harst, Pim, Jukema, J. Wouter, Ikram, M. Arfan, Blangero, John, Kooperberg, Charles, Desch, Karl C., Johnson, Andrew D., Sabater-Lleal, Maria, Lowenstein, Charles J., Smith, Nicholas L., and Morrison, Alanna C.
Abstract: •We identified 7 new genetic regions for factor VIII levels, 1 for von Willebrand factor levels, and 3 in a combined analysis.•Silencing B3GNT2and CD36reduced factor VIII release in vitro.Silencing B3GNT2, CD36, and PDIA3reduced von Willebrand factor release.
Published: 2024
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17. Heritability of Atrial Fibrillation.

Author: Lu-Chen Weng, Seung Hoan Choi, Klarin, Derek, Smith, J. Gustav, Po-Ru Loh, Chaffin, Mark, Roselli, Carolina, Hulme, Olivia L., Lunetta, Kathryn L., Dupuis, Josée, Benjamin, Emelia J., Newton-Cheh, Christopher, Kathiresan, Sekar, Ellinor, Patrick T., and Lubitz, Steven A.
Abstract: Background--Previous reports have implicated multiple genetic loci associated with AF, but the contributions of genomewide variation to AF susceptibility have not been quantified. Methods and Results--We assessed the contribution of genome-wide single-nucleotide polymorphism variation to AF risk (single-nucleotide polymorphism heritability, h2g) using data from 120 286 unrelated individuals of European ancestry (2987 with AF) in the population-based UK Biobank. We ascertained AF based on self-report, medical record billing codes, procedure codes, and death records. We estimated h2g using a variance components method with variants having a minor allele frequency ≥1%. We evaluated h2g in age, sex, and genomic strata of interest. The h2g for AF was 22.1% (95% confidence interval, 15.6%-28.5%) and was similar for early-versus older-onset AF (≤65 versus >65 years of age), as well as for men and women. The proportion of AF variance explained by genetic variation was mainly accounted for by common (minor allele frequency, ≥5%) variants (20.4%; 95% confidence interval, 15.1%-25.6%). Only 6.4% (95% confidence interval, 5.1%-7.7%) of AF variance was attributed to variation within known AF susceptibility, cardiac arrhythmia, and cardiomyopathy gene regions. Conclusions--Genetic variation contributes substantially to AF risk. The risk for AF conferred by genomic variation is similar to that observed for several other cardiovascular diseases. Established AF loci only explain a moderate proportion of disease risk, suggesting that further genetic discovery, with an emphasis on common variation, is warranted to understand the causal genetic basis of AF. [ABSTRACT FROM AUTHOR]
Published: 2017
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18. Genome-wide association analysis of venous thromboembolism identifies new risk loci and genetic overlap with arterial vascular disease

Author: Klarin, Derek, Busenkell, Emma, Judy, Renae, Lynch, Julie, Levin, Michael, Haessler, Jeffery, Aragam, Krishna, Chaffin, Mark, Haas, Mary, Lindström, Sara, Assimes, Themistocles L., Huang, Jie, Min Lee, Kyung, Shao, Qing, Huffman, Jennifer E., Kabrhel, Christopher, Huang, Yunfeng, Sun, Yan V., Vujkovic, Marijana, Saleheen, Danish, Miller, Donald R., Reaven, Peter, DuVall, Scott, Boden, William E., Pyarajan, Saiju, Reiner, Alex P., Trégouët, David-Alexandre, Henke, Peter, Kooperberg, Charles, Gaziano, J. Michael, Concato, John, Rader, Daniel J., Cho, Kelly, Chang, Kyong-Mi, Wilson, Peter W. F., Smith, Nicholas L., O’Donnell, Christopher J., Tsao, Philip S., Kathiresan, Sekar, Obi, Andrea, Damrauer, Scott M., and Natarajan, Pradeep
Abstract: Venous thromboembolism is a significant cause of mortality1, yet its genetic determinants are incompletely defined. We performed a discovery genome-wide association study in the Million Veteran Program and UK Biobank, with testing of approximately 13 million DNA sequence variants for association with venous thromboembolism (26,066 cases and 624,053 controls) and meta-analyzed both studies, followed by independent replication with up to 17,672 venous thromboembolism cases and 167,295 controls. We identified 22 previously unknown loci, bringing the total number of venous thromboembolism-associated loci to 33, and subsequently fine-mapped these associations. We developed a genome-wide polygenic risk score for venous thromboembolism that identifies 5% of the population at an equivalent incident venous thromboembolism risk to carriers of the established factor V Leiden p.R506Q and prothrombin G20210A mutations. Our data provide mechanistic insights into the genetic epidemiology of venous thromboembolism and suggest a greater overlap among venous and arterial cardiovascular disease than previously thought.
Published: 2019
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19. Leveraging Human Genetics to Estimate Clinical Risk Reductions Achievable by Inhibiting Factor XI

Author: Georgi, Benjamin, Mielke, Johanna, Chaffin, Mark, Khera, Amit V., Gelis, Lian, Mundl, Hardi, van Giezen, J.J.J., Ellinor, Patrick, Kathiresan, Sekar, Ziegelbauer, Karl, and Freitag, Daniel F.
Abstract: Supplemental Digital Content is available in the text.
Published: 2019
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20. Genome-wide association study of peripheral artery disease in the Million Veteran Program

Author: Klarin, Derek, Lynch, Julie, Aragam, Krishna, Chaffin, Mark, Assimes, Themistocles, Huang, Jie, Lee, Kyung, Shao, Qing, Huffman, Jennifer, Natarajan, Pradeep, Arya, Shipra, Small, Aeron, Sun, Yan, Vujkovic, Marijana, Freiberg, Matthew, Wang, Lu, Chen, Jinbo, Saleheen, Danish, Lee, Jennifer, Miller, Donald, Reaven, Peter, Alba, Patrick, Patterson, Olga, DuVall, Scott, Boden, William, Beckman, Joshua, Gaziano, J., Concato, John, Rader, Daniel, Cho, Kelly, Chang, Kyong-Mi, Wilson, Peter, O’Donnell, Christopher, Kathiresan, Sekar, Tsao, Philip, and Damrauer, Scott
Abstract: Peripheral artery disease (PAD) is a leading cause of cardiovascular morbidity and mortality; however, the extent to which genetic factors increase risk for PAD is largely unknown. Using electronic health record data, we performed a genome-wide association study in the Million Veteran Program testing ~32 million DNA sequence variants with PAD (31,307 cases and 211,753 controls) across veterans of European, African and Hispanic ancestry. The results were replicated in an independent sample of 5,117 PAD cases and 389,291 controls from the UK Biobank. We identified 19 PAD loci, 18 of which have not been previously reported. Eleven of the 19 loci were associated with disease in three vascular beds (coronary, cerebral, peripheral), including LDLR, LPLand LPA, suggesting that therapeutic modulation of low-density lipoprotein cholesterol, the lipoprotein lipase pathway or circulating lipoprotein(a) may be efficacious for multiple atherosclerotic disease phenotypes. Conversely, four of the variants appeared to be specific for PAD, including F5p.R506Q, highlighting the pathogenic role of thrombosis in the peripheral vascular bed and providing genetic support for Factor Xa inhibition as a therapeutic strategy for PAD. Our results highlight mechanistic similarities and differences among coronary, cerebral and peripheral atherosclerosis and provide therapeutic insights. A genome-wide association study using electronic health record data in the Million Veteran Program uncovers new genetic loci associated specifically with peripheral artery disease, as compared to stroke or coronary artery disease.
Published: 2019
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21. MetProc: Separating Measurement Artifacts from True Metabolites in an Untargeted Metabolomics Experiment

Author: Chaffin, Mark D., Cao, Liu, Deik, Amy A., Clish, Clary B., Hu, Frank B., Martínez-González, Miguel A., Razquin, Cristina, Bullo, Monica, Corella, Dolores, Gómez-Gracia, Enrique, Fiol, Miquel, Estruch, Ramon, Lapetra, José, Fitó, Montserrat, Arós, Fernando, Serra-Majem, Lluís, Ros, Emilio, and Liang, Liming
Abstract: High-throughput metabolomics using liquid chromatography and mass spectrometry (LC/MS) provides a useful method to identify biomarkers of disease and explore biological systems. However, the majority of metabolic features detected from untargeted metabolomics experiments have unknown ion signatures, making it critical that data should be thoroughly quality controlled to avoid analyzing false signals. Here, we present a postalignment method relying on intermittent pooled study samples to separate genuine metabolic features from potential measurement artifacts. We apply the method to lipid metabolite data from the PREDIMED (PREvención con DIeta MEDi-terránea) study to demonstrate clear removal of measurement artifacts. The method is publicly available as the R package MetProc, available on CRAN under the GPL-v2 license.
Published: 2019
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22. Genetic Variation in Cardiometabolic Traits and Medication Targets and the Risk of Hypertensive Disorders of Pregnancy

Author: Honigberg, Michael C., Chaffin, Mark, Aragam, Krishna, Bhatt, Deepak L., Wood, Malissa J., Sarma, Amy A., Scott, Nandita S., Peloso, Gina M., and Natarajan, Pradeep
Published: 2020
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23. Myocyte-Specific Upregulation of ACE2in Cardiovascular Disease

Author: Tucker, Nathan R., Chaffin, Mark, Bedi, Kenneth C., Papangeli, Irinna, Akkad, Amer-Denis, Arduini, Alessandro, Hayat, Sikander, Eraslan, Gökcen, Muus, Christoph, Bhattacharyya, Roby P., Stegmann, Christian M., Margulies, Kenneth B., and Ellinor, Patrick T.
Abstract: Supplemental Digital Content is available in the text.
Published: 2020
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24. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program

Author: Klarin, Derek, Damrauer, Scott M., Cho, Kelly, Sun, Yan V., Teslovich, Tanya M., Honerlaw, Jacqueline, Gagnon, David R., DuVall, Scott L., Li, Jin, Peloso, Gina M., Chaffin, Mark, Small, Aeron M., Huang, Jie, Tang, Hua, Lynch, Julie A., Ho, Yuk-Lam, Liu, Dajiang J., Emdin, Connor A., Li, Alexander H., Huffman, Jennifer E., Lee, Jennifer S., Natarajan, Pradeep, Chowdhury, Rajiv, Saleheen, Danish, Vujkovic, Marijana, Baras, Aris, Pyarajan, Saiju, Di Angelantonio, Emanuele, Neale, Benjamin M., Naheed, Aliya, Khera, Amit V., Danesh, John, Chang, Kyong-Mi, Abecasis, Gonçalo, Willer, Cristen, Dewey, Frederick E., Carey, David J., Concato, John, Gaziano, J. Michael, O’Donnell, Christopher J., Tsao, Philip S., Kathiresan, Sekar, Rader, Daniel J., Wilson, Peter W. F., and Assimes, Themistocles L.
Abstract: The Million Veteran Program (MVP) was established in 2011 as a national research initiative to determine how genetic variation influences the health of US military veterans. Here we genotyped 312,571 MVP participants using a custom biobank array and linked the genetic data to laboratory and clinical phenotypes extracted from electronic health records covering a median of 10.0 years of follow-up. Among 297,626 veterans with at least one blood lipid measurement, including 57,332 black and 24,743 Hispanic participants, we tested up to around 32 million variants for association with lipid levels and identified 118 novel genome-wide significant loci after meta-analysis with data from the Global Lipids Genetics Consortium (total n> 600,000). Through a focus on mutations predicted to result in a loss of gene function and a phenome-wide association study, we propose novel indications for pharmaceutical inhibitors targeting PCSK9 (abdominal aortic aneurysm), ANGPTL4 (type 2 diabetes) and PDE3B (triglycerides and coronary disease).
Published: 2018
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25. An APOOPseudogene on Chromosome 5q Is Associated With Low-Density Lipoprotein Cholesterol Levels

Author: Montasser, May E., O’Hare, Elizabeth A., Wang, Xiaochun, Howard, Alicia D., McFarland, Rebecca, Perry, James A., Ryan, Kathleen A., Rice, Kenneth, Jaquish, Cashell E., Shuldiner, Alan R., Miller, Michael, Mitchell, Braxton D., Zaghloul, Norann A., Chang, Yen-Pei C., Abe, Namiko, Abecasis, Goncalo, Albert, Christine, Allred, Nicholette P., Almasy, Laura, Alonso, Alvaro, Ament, Seth, Anderson, Peter, Anugu, Pramod, Applebaum-Bowden, Debora, Arking, Dan, Arnett, Donna K, Ashley-Koch, Allison, Aslibekyan, Stella, Assimes, Tim, Auer, Paul, Avramopoulos, Dimitrios, Barnard, John, Barnes, Kathleen, Graham Barr, R., Barron-Casella, Emily, Beaty, Terri, Becker, Diane, Becker, Lewis, Beer, Rebecca, Begum, Ferdouse, Beitelshees, Amber, Benjamin, Emelia, Bezerra, Marcos, Bielak, Larry, Bis, Joshua, Blackwell, Thomas, Blangero, John, Boerwinkle, Eric, Borecki, Ingrid, Bowler, Russel, Brody, Jennifer, Broeckel, Ulrich, Broome, Jai, Bunting, Karen, Burchard, Esteban, Cardwell, Jonathan, Carlson, Sara, Carty, Cara, Casaburi, Richard, Casella, James, Chaffin, Mark, Chang, Christy, Chasman, Daniel, Chavan, Sameer, Chen, Bo-Juen, Chen, Wei-Min, Ida Chen, Yii-Der, Cho, Michael, Choi, Seung Hoan, Chuang, Lee-Ming, Chung, Mina, Cornell, Elaine, Correa, Adolfo, Crandall, Carolyn, Crapo, James, Cupples, L Adrienne, Curran, Joanne, Curtis, Jeffrey, Custer, Brian, Damcott, Coleen, Darbar, Dawood, Das, Sayantan, David, Sean, Davis, Colleen, Daya, Michelle, de Andrade, Mariza, DeBaun, Michael, Deka, Ranjan, DeMeo, Dawn, Devine, Scott, Do, Ron, Duan, Qing, Duggirala, Ravi, Durda, Peter, Dutcher, Susan, Eaton, Charles, Ekunwe, Lynette, Ellinor, Patrick, Emery, Leslie, Farber, Charles, Farnam, Leanna, Fingerlin, Tasha, Flickinger, Matthew, Fornage, Myriam, Franceschini, Nora, Fu, Mao, Fullerton, Malia, Fulton, Lucinda, Gabriel, Stacey, Gan, Weiniu, Gao, Yan, Gass, Margery, Gelb, Bruce, Geng, Xiaoqi (Priscilla), Germer, Soren, Gignoux, Chris, Gladwin, Mark, Glahn, David, Gogarten, Stephanie, Gong, Da-Wei, Goring, Harald, Charles Gu, C., Guan, Yue, Guo, Xiuqing, Haessler, Jeff, Hall, Michael, Harris, Daniel, Hawley, Nicola, He, Jiang, Heavner, Ben, Heckbert, Susan, Hernandez, Ryan, Herrington, David, Hersh, Craig, Hidalgo, Bertha, Hixson, James, Hokanson, John, Hong, Elliott, Hoth, Karin, Hsiung, Chao (Agnes), Huston, Haley, Hwu, Chii Min, Irvin, Marguerite, Jackson, Rebecca, Jain, Deepti, Jaquish, Cashell, Jhun, Min A, Johnsen, Jill, Johnson, Andrew, Johnson, Craig, Johnston, Rich, Jones, Kimberly, Kang, Hyun Min, Kaplan, Robert, Kardia, Sharon, Kathiresan, Sekar, Kaufman, Laura, Kelly, Shannon, Kenny, Eimear, Kessler, Michael, Khan, Alyna, Kinney, Greg, Konkle, Barbara, Kooperberg, Charles, Kramer, Holly, Krauter, Stephanie, Lange, Christoph, Lange, Ethan, Lange, Leslie, Laurie, Cathy, Laurie, Cecelia, LeBoff, Meryl, Lee, Seunggeun, Lee, Wen-Jane, LeFaive, Jonathon, Levine, David, Levy, Dan, Lewis, Joshua, Li, Yun, Lin, Honghuang, Lin, Keng Han, Liu, Simin, Liu, Yongmei, Loos, Ruth, Lunetta, Kathryn, Luo, James, Mahaney, Michael, Make, Barry, Manichaikul, Ani, Manson, JoAnn, Margolin, Lauren, Martin, Lisa, Mathai, Susan, Mathias, Rasika, McArdle, Patrick, McDonald, Merry-Lynn, McFarland, Sean, McGarvey, Stephen, Mei, Hao, Meyers, Deborah, Mikulla, Julie, Min, Nancy, Minear, Mollie, Minster, Ryan L, Mitchell, Braxton, Musani, Solomon, Mwasongwe, Stanford, Mychaleckyj, Josyf C, Nadkarni, Girish, Naik, Rakhi Johns, Natarajan, Pradeep, Nekhai, Sergei, Nickerson, Deborah, North, Kari, O’Connell, Jeff, O’Connor, Tim, Heather, Ochs-Balcom, Pankow, James, Papanicolaou, George, Parker, Margaret, Parsa, Afshin, Pattison, Jessica Tangarone, Penchev, Sara, Peralta, Juan Manuel, Perez, Marco, Perry, James, Peters, Ulrike, Peyser, Patricia, Phillips, Larry, Phillips, Sam, Pollin, Toni, Post, Wendy, Becker, Julia Powers, Boorgula, Meher Preethi, Preuss, Michael, Prokopenko, Dmitry, Psaty, Bruce, Qasba, Pankaj, Qiao, Dandi, Qin, Zhaohui, Rafaels, Nicholas, Raffield, Laura, Ramachandran, Vasan, Rao, D.C., Rasmussen-Torvik, Laura, Ratan, Aakrosh, Redline, Susan, Reed, Robert, Regan, Elizabeth, Reiner, Alex, Rice, Ken, Rich, Stephen, Roden, Dan, Roselli, Carolina, Rotter, Jerome, Ruczinski, Ingo, Russell, Pamela, Ruuska, Sarah, Ryan, Kathleen, Sakornsakolpat, Phuwanat, Salimi, Shabnam, Salzberg, Steven, Sandow, Kevin, Sankaran, Vijay, Scheller, Christopher, Schmidt, Ellen, Schwander, Karen, Schwartz, David, Sciurba, Frank, Seidman, Christine, Sheehan, Vivien, Shetty, Amol, Shetty, Aniket, Hui-Heng Sheu, Wayne, Benjamin Shoemaker, M., Silver, Brian, Silverman, Edwin, Smith, Jennifer, Smith, Josh, Smith, Nicholas, Smith, Tanja, Smoller, Sylvia, Snively, Beverly, Sofer, Tamar, Sotoodehnia, Nona, Stilp, Adrienne, Streeten, Elizabeth, Sung, Yun Ju, Sylvia, Jody, Szpiro, Adam, Sztalryd, Carole, Taliun, Daniel, Tang, Hua, Taub, Margaret, Taylor, Kent, Taylor, Simeon, Telen, Marilyn, Thornton, Timothy A., Tinker, Lesley, Tirschwell, David, Tiwari, Hemant, Tracy, Russell, Tsai, Michael, Vaidya, Dhananjay, VandeHaar, Peter, Vrieze, Scott, Walker, Tarik, Wallace, Robert, Walts, Avram, Wan, Emily, Fei Wang, Fei, Watson, Karol, Weeks, Daniel E., Weir, Bruce, Weiss, Scott, Weng, Lu-Chen, Willer, Cristen, Williams, Kayleen, Keoki Williams, L., Wilson, Carla, Wilson, James, Wong, Quenna, Xu, Huichun, Yanek, Lisa, Yang, Ivana, Yang, Rongze, Zaghloul, Norann, Zhang, Yingze, Zhao, Snow Xueyan, Zhao, Wei, Zheng, Xiuwen, Zhi, Degui, Zhou, Xiang, Zody, Michael, and Zoellner, Sebastian
Abstract: Supplemental Digital Content is available in the text.
Published: 2018
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26. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

Author: Khera, Amit V., Chaffin, Mark, Aragam, Krishna G., Haas, Mary E., Roselli, Carolina, Choi, Seung Hoan, Natarajan, Pradeep, Lander, Eric S., Lubitz, Steven A., Ellinor, Patrick T., and Kathiresan, Sekar
Abstract: A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation1. Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature2–5, it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk6. We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care, and discuss relevant issues.
Published: 2018
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27. Multi-ethnic genome-wide association study for atrial fibrillation

Author: Roselli, Carolina, Chaffin, Mark D., Weng, Lu-Chen, Aeschbacher, Stefanie, Ahlberg, Gustav, Albert, Christine M., Almgren, Peter, Alonso, Alvaro, Anderson, Christopher D., Aragam, Krishna G., Arking, Dan E., Barnard, John, Bartz, Traci M., Benjamin, Emelia J., Bihlmeyer, Nathan A., Bis, Joshua C., Bloom, Heather L., Boerwinkle, Eric, Bottinger, Erwin B., Brody, Jennifer A., Calkins, Hugh, Campbell, Archie, Cappola, Thomas P., Carlquist, John, Chasman, Daniel I., Chen, Lin Y., Chen, Yii-Der Ida, Choi, Eue-Keun, Choi, Seung Hoan, Christophersen, Ingrid E., Chung, Mina K., Cole, John W., Conen, David, Cook, James, Crijns, Harry J., Cutler, Michael J., Damrauer, Scott M., Daniels, Brian R., Darbar, Dawood, Delgado, Graciela, Denny, Joshua C., Dichgans, Martin, Dörr, Marcus, Dudink, Elton A., Dudley, Samuel C., Esa, Nada, Esko, Tonu, Eskola, Markku, Fatkin, Diane, Felix, Stephan B., Ford, Ian, Franco, Oscar H., Geelhoed, Bastiaan, Grewal, Raji P., Gudnason, Vilmundur, Guo, Xiuqing, Gupta, Namrata, Gustafsson, Stefan, Gutmann, Rebecca, Hamsten, Anders, Harris, Tamara B., Hayward, Caroline, Heckbert, Susan R., Hernesniemi, Jussi, Hocking, Lynne J., Hofman, Albert, Horimoto, Andrea R. V. R., Huang, Jie, Huang, Paul L., Huffman, Jennifer, Ingelsson, Erik, Ipek, Esra Gucuk, Ito, Kaoru, Jimenez-Conde, Jordi, Johnson, Renee, Jukema, J. Wouter, Kääb, Stefan, Kähönen, Mika, Kamatani, Yoichiro, Kane, John P., Kastrati, Adnan, Kathiresan, Sekar, Katschnig-Winter, Petra, Kavousi, Maryam, Kessler, Thorsten, Kietselaer, Bas L., Kirchhof, Paulus, Kleber, Marcus E., Knight, Stacey, Krieger, Jose E., Kubo, Michiaki, Launer, Lenore J., Laurikka, Jari, Lehtimäki, Terho, Leineweber, Kirsten, Lemaitre, Rozenn N., Li, Man, Lim, Hong Euy, Lin, Henry J., Lin, Honghuang, Lind, Lars, Lindgren, Cecilia M., Lokki, Marja-Liisa, London, Barry, Loos, Ruth J. F., Low, Siew-Kee, Lu, Yingchang, Lyytikäinen, Leo-Pekka, Macfarlane, Peter W., Magnusson, Patrik K., Mahajan, Anubha, Malik, Rainer, Mansur, Alfredo J., Marcus, Gregory M., Margolin, Lauren, Margulies, Kenneth B., März, Winfried, McManus, David D., Melander, Olle, Mohanty, Sanghamitra, Montgomery, Jay A., Morley, Michael P., Morris, Andrew P., Müller-Nurasyid, Martina, Natale, Andrea, Nazarian, Saman, Neumann, Benjamin, Newton-Cheh, Christopher, Niemeijer, Maartje N., Nikus, Kjell, Nilsson, Peter, Noordam, Raymond, Oellers, Heidi, Olesen, Morten S., Orho-Melander, Marju, Padmanabhan, Sandosh, Pak, Hui-Nam, Paré, Guillaume, Pedersen, Nancy L., Pera, Joanna, Pereira, Alexandre, Porteous, David, Psaty, Bruce M., Pulit, Sara L., Pullinger, Clive R., Rader, Daniel J., Refsgaard, Lena, Ribasés, Marta, Ridker, Paul M., Rienstra, Michiel, Risch, Lorenz, Roden, Dan M., Rosand, Jonathan, Rosenberg, Michael A., Rost, Natalia, Rotter, Jerome I., Saba, Samir, Sandhu, Roopinder K., Schnabel, Renate B., Schramm, Katharina, Schunkert, Heribert, Schurman, Claudia, Scott, Stuart A., Seppälä, Ilkka, Shaffer, Christian, Shah, Svati, Shalaby, Alaa A., Shim, Jaemin, Shoemaker, M. Benjamin, Siland, Joylene E., Sinisalo, Juha, Sinner, Moritz F., Slowik, Agnieszka, Smith, Albert V., Smith, Blair H., Smith, J. Gustav, Smith, Jonathan D., Smith, Nicholas L., Soliman, Elsayed Z., Sotoodehnia, Nona, Stricker, Bruno H., Sun, Albert, Sun, Han, Svendsen, Jesper H., Tanaka, Toshihiro, Tanriverdi, Kahraman, Taylor, Kent D., Teder-Laving, Maris, Teumer, Alexander, Thériault, Sébastien, Trompet, Stella, Tucker, Nathan R., Tveit, Arnljot, Uitterlinden, Andre G., Van Der Harst, Pim, Van Gelder, Isabelle C., Van Wagoner, David R., Verweij, Niek, Vlachopoulou, Efthymia, Völker, Uwe, Wang, Biqi, Weeke, Peter E., Weijs, Bob, Weiss, Raul, Weiss, Stefan, Wells, Quinn S., Wiggins, Kerri L., Wong, Jorge A., Woo, Daniel, Worrall, Bradford B., Yang, Pil-Sung, Yao, Jie, Yoneda, Zachary T., Zeller, Tanja, Zeng, Lingyao, Lubitz, Steven A., Lunetta, Kathryn L., and Ellinor, Patrick T.
Abstract: Atrial fibrillation (AF) affects more than 33 million individuals worldwide1and has a complex heritability2. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF.
Published: 2018
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28. A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases

Author: Zhu, Zhaozhong, Lee, Phil, Chaffin, Mark, Chung, Wonil, Loh, Po-Ru, Lu, Quan, Christiani, David, and Liang, Liming
Abstract: Clinical and epidemiological data suggest that asthma and allergic diseases are associated and may share a common genetic etiology. We analyzed genome-wide SNP data for asthma and allergic diseases in 33,593 cases and 76,768 controls of European ancestry from UK Biobank. Two publicly available independent genome-wide association studies were used for replication. We have found a strong genome-wide genetic correlation between asthma and allergic diseases (rg= 0.75, P= 6.84 × 10−62). Cross-trait analysis identified 38 genome-wide significant loci, including 7 novel shared loci. Computational analysis showed that shared genetic loci are enriched in immune/inflammatory systems and tissues with epithelium cells. Our work identifies common genetic architectures shared between asthma and allergy and will help to advance understanding of the molecular mechanisms underlying co-morbid asthma and allergic diseases. Genome-wide cross-trait analysis shows a strong genetic correlation between asthma and allergic diseases. Shared susceptibility loci are enriched for genes involved in immune and inflammatory responses and genes expressed in epithelial tissues.
Published: 2018
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29. Genetic analysis in UK Biobank links insulin resistance and transendothelial migration pathways to coronary artery disease

Author: Klarin, Derek, Zhu, Qiuyu Martin, Emdin, Connor A, Chaffin, Mark, Horner, Steven, McMillan, Brian J, Leed, Alison, Weale, Michael E, Spencer, Chris C A, Aguet, François, Segrè, Ayellet V, Ardlie, Kristin G, Khera, Amit V, Kaushik, Virendar K, Natarajan, Pradeep, and Kathiresan, Sekar
Abstract: UK Biobank is among the world's largest repositories for phenotypic and genotypic information in individuals of European ancestry. We performed a genome-wide association study in UK Biobank testing ∼9 million DNA sequence variants for association with coronary artery disease (4,831 cases and 115,455 controls) and carried out meta-analysis with previously published results. We identified 15 new loci, bringing the total number of loci associated with coronary artery disease to 95 at the time of analysis. Phenome-wide association scanning showed that CCDC92 likely affects coronary artery disease through insulin resistance pathways, whereas experimental analysis suggests that ARHGEF26 influences the transendothelial migration of leukocytes.
Published: 2017
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30. Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation

Author: Christophersen, Ingrid E, Rienstra, Michiel, Roselli, Carolina, Yin, Xiaoyan, Geelhoed, Bastiaan, Barnard, John, Lin, Honghuang, Arking, Dan E, Smith, Albert V, Albert, Christine M, Chaffin, Mark, Tucker, Nathan R, Li, Molong, Klarin, Derek, Bihlmeyer, Nathan A, Low, Siew-Kee, Weeke, Peter E, Müller-Nurasyid, Martina, Smith, J Gustav, Brody, Jennifer A, Niemeijer, Maartje N, Dörr, Marcus, Trompet, Stella, Huffman, Jennifer, Gustafsson, Stefan, Schurmann, Claudia, Kleber, Marcus E, Lyytikäinen, Leo-Pekka, Seppälä, Ilkka, Malik, Rainer, Horimoto, Andrea R V R, Perez, Marco, Sinisalo, Juha, Aeschbacher, Stefanie, Thériault, Sébastien, Yao, Jie, Radmanesh, Farid, Weiss, Stefan, Teumer, Alexander, Choi, Seung Hoan, Weng, Lu-Chen, Clauss, Sebastian, Deo, Rajat, Rader, Daniel J, Shah, Svati H, Sun, Albert, Hopewell, Jemma C, Debette, Stephanie, Chauhan, Ganesh, Yang, Qiong, Worrall, Bradford B, Paré, Guillaume, Kamatani, Yoichiro, Hagemeijer, Yanick P, Verweij, Niek, Siland, Joylene E, Kubo, Michiaki, Smith, Jonathan D, Van Wagoner, David R, Bis, Joshua C, Perz, Siegfried, Psaty, Bruce M, Ridker, Paul M, Magnani, Jared W, Harris, Tamara B, Launer, Lenore J, Shoemaker, M Benjamin, Padmanabhan, Sandosh, Haessler, Jeffrey, Bartz, Traci M, Waldenberger, Melanie, Lichtner, Peter, Arendt, Marina, Krieger, Jose E, Kähönen, Mika, Risch, Lorenz, Mansur, Alfredo J, Peters, Annette, Smith, Blair H, Lind, Lars, Scott, Stuart A, Lu, Yingchang, Bottinger, Erwin B, Hernesniemi, Jussi, Lindgren, Cecilia M, Wong, Jorge A, Huang, Jie, Eskola, Markku, Morris, Andrew P, Ford, Ian, Reiner, Alex P, Delgado, Graciela, Chen, Lin Y, Chen, Yii-Der Ida, Sandhu, Roopinder K, Li, Man, Boerwinkle, Eric, Eisele, Lewin, Lannfelt, Lars, Rost, Natalia, Anderson, Christopher D, Taylor, Kent D, Campbell, Archie, Magnusson, Patrik K, Porteous, David, Hocking, Lynne J, Vlachopoulou, Efthymia, Pedersen, Nancy L, Nikus, Kjell, Orho-Melander, Marju, Hamsten, Anders, Heeringa, Jan, Denny, Joshua C, Kriebel, Jennifer, Darbar, Dawood, Newton-Cheh, Christopher, Shaffer, Christian, Macfarlane, Peter W, Heilmann-Heimbach, Stefanie, Almgren, Peter, Huang, Paul L, Sotoodehnia, Nona, Soliman, Elsayed Z, Uitterlinden, Andre G, Hofman, Albert, Franco, Oscar H, Völker, Uwe, Jöckel, Karl-Heinz, Sinner, Moritz F, Lin, Henry J, Guo, Xiuqing, Dichgans, Martin, Ingelsson, Erik, Kooperberg, Charles, Melander, Olle, Loos, Ruth J F, Laurikka, Jari, Conen, David, Rosand, Jonathan, van der Harst, Pim, Lokki, Marja-Liisa, Kathiresan, Sekar, Pereira, Alexandre, Jukema, J Wouter, Hayward, Caroline, Rotter, Jerome I, März, Winfried, Lehtimäki, Terho, Stricker, Bruno H, Chung, Mina K, Felix, Stephan B, Gudnason, Vilmundur, Alonso, Alvaro, Roden, Dan M, Kääb, Stefan, Chasman, Daniel I, Heckbert, Susan R, Benjamin, Emelia J, Tanaka, Toshihiro, Lunetta, Kathryn L, Lubitz, Steven A, and Ellinor, Patrick T
Abstract: Atrial fibrillation affects more than 33 million people worldwide and increases the risk of stroke, heart failure, and death. Fourteen genetic loci have been associated with atrial fibrillation in European and Asian ancestry groups. To further define the genetic basis of atrial fibrillation, we performed large-scale, trans-ancestry meta-analyses of common and rare variant association studies. The genome-wide association studies (GWAS) included 17,931 individuals with atrial fibrillation and 115,142 referents; the exome-wide association studies (ExWAS) and rare variant association studies (RVAS) involved 22,346 cases and 132,086 referents. We identified 12 new genetic loci that exceeded genome-wide significance, implicating genes involved in cardiac electrical and structural remodeling. Our results nearly double the number of known genetic loci for atrial fibrillation, provide insights into the molecular basis of atrial fibrillation, and may facilitate the identification of new potential targets for drug discovery.
Published: 2017
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31. Single-nucleus RNA sequencing in ischemic cardiomyopathy reveals common transcriptional profile underlying end-stage heart failure

Author: Simonson, Bridget, Chaffin, Mark, Hill, Matthew C., Atwa, Ondine, Guedira, Yasmine, Bhasin, Harshit, Hall, Amelia W., Hayat, Sikander, Baumgart, Simon, Bedi, Kenneth C., Margulies, Kenneth B., Klattenhoff, Carla A., and Ellinor, Patrick T.
Abstract: Ischemic cardiomyopathy (ICM) is the leading cause of heart failure worldwide, yet the cellular and molecular signature of this disease is largely unclear. Using single-nucleus RNA sequencing (snRNA-seq) and integrated computational analyses, we profile the transcriptomes of over 99,000 human cardiac nuclei from the non-infarct region of the left ventricle of 7 ICM transplant recipients and 8 non-failing (NF) controls. We find the cellular composition of the ischemic heart is significantly altered, with decreased cardiomyocytes and increased proportions of lymphatic, angiogenic, and arterial endothelial cells in patients with ICM. We show that there is increased LAMININ signaling from endothelial cells to other cell types in ICM compared with NF. Finally, we find that the transcriptional changes that occur in ICM are similar to those in hypertrophic and dilated cardiomyopathies and that the mining of these combined datasets can identify druggable genes that could be used to target end-stage heart failure.
Published: 2023
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32. A Combined Motivation and Parent-Child Interaction Therapy Package Reduces Child Welfare Recidivism in a Randomized Dismantling Field Trial.

Author: Chaffin, Mark, Funderburk, Beverly, Bard, David, Valle, Linda Anne, and Gurwitch, Robin
Subjects: PARENT-child interaction therapy, PARENT-child communication, RECIDIVISM, PARENTAL deprivation, RANDOMIZED controlled trials, CHILD welfare, FIELD research, STATISTICAL matching
Abstract: Objective: A package of parent-child interaction therapy (PCIT) combined with a self-motivational (SM) orientation previously was found in a laboratory trial to reduce child abuse recidivism compared with services as usual (SAU). Objectives of the present study were to test effectiveness in a field agency rather than in a laboratory setting and to dismantle the SM versus SAU orientation and PCIT versus SAU parenting component effects. Method: Participants were 192 parents in child welfare with an average of 6 prior referrals and most with all of their children removed. Following a 2 × 2 sequentially randomized experimental design, parents were randomized first to orientation condition (SM vs. SAU) and then subsequently randomized to a parenting condition (PCIT vs. SAU). Cases were followed for child welfare recidivism for a median of 904 days. An imputation-based approach was used to estimate recidivism survival complicated by significant treatment-related differences in timing and frequency of children returned home. Results: A significant orientation condition by parenting condition interaction favoring the SM + PCIT combination was found for reducing future child welfare reports, and this effect was stronger when children were returned to the home sooner rather than later. Conclusions: Findings demonstrate that previous laboratory results can be replicated in a field implementation setting and among parents with chronic and severe child welfare histories, supporting a synergistic SM + PCIT benefit. Methodological considerations for analyzing child welfare event history data complicated by differential risk deprivation are also emphasized. [ABSTRACT FROM AUTHOR]
Published: 2011
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33. The Impact of Evidence-Based Practice Implementation and Fidelity Monitoring on Staff Turnover: Evidence for a Protective Effect.

Author: Aarons, Gregory A., Sommerfeld, David H., Hecht, Debra B., Silovsky, Jane F., and Chaffin, Mark J.
Subjects: EMPLOYEE retention, MENTAL health facility employees, MENTAL health services, SOCIAL services, LABOR supply, STRATEGIC planning, LABOR turnover, CHILD care
Abstract: Staff retention is an ongoing challenge in mental health and community-based service organizations. Little is known about the impact of evidence-based practice implementation on the mental health and social service workforce. The present study examined the effect of evidence-based practice implementation and ongoing fidelity monitoring on staff retention in a children's services system. The study took place in the context of a statewide, regionally randomized effectiveness trial of an evidence-based intervention designed to reduce child neglect. In the study 21 teams consisting of 153 home-based service providers were followed over a 29-month period. Survival analyses revealed greater staff retention in the condition where the evidence-based practice was implemented along with ongoing fidelity monitoring presented to staff as supportive consultation. These results should help to allay concerns about staff retention when implementing evidence-based practices where there is good values-innovation fit and when fidelity monitoring is designed as an aid and support to service providers in providing a high standard of care for children and families. [ABSTRACT FROM AUTHOR]
Published: 2009
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34. Randomized Trial of Treatment for Children With Sexual Behavior Problems: Ten-Year Follow-Up.

Author: Carpentier, Melissa Y., Silovsky, Jane F., and Chaffin, Mark
Subjects: BEHAVIOR disorders in children, RANDOMIZED controlled trials, CHILDREN'S sexual behavior, COGNITIVE therapy, COMPARISON (Psychology), GROUP play therapy, CHILD psychotherapy, SEX crimes, CHILD welfare
Abstract: This study prospectively follows 135 children 5-12 years of age with sexual behavior problems from a randomized trial comparing a 12-session group cognitive-behavioral therapy (CBT) with group play therapy and follows 156 general clinic children with nonsexual behavior problems. Ten-year follow-up data on future juvenile and adult arrests and child welfare perpetration reports were collected. The CBT group had significantly fewer future sex offenses than the play therapy group (2% vs. 10%) and did not differ from the general clinic comparison (3%), supporting the use of short-term CBT. There were no group differences in nonsexual offenses (21%). The findings do not support assumptions about persistent or difficult to modify risk and raise questions about policies and practices founded on this assumption. [ABSTRACT FROM AUTHOR]
Published: 2006
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35. Health services use and growth patterns among older siblings of infants with prenatal drug exposure.

Author: Bertsch, Christina M., Mullins, Sharon M., and Chaffin, Mark
Abstract: Abstract: The health care use patterns, suspected maltreatment, and growth trajectories of siblings of infants with prenatal drug exposure were examined. Ninety-three siblings of drug-exposed infants were matched with patients from the same primary care clinic. The case patients were found to have significantly fewer health care contacts, more deficient immunizations, and more reports of suspected child maltreatment. Although they did not differ in birth weight, growth curve modeling found that case patients had lower weight gain trajectories. When drug-exposed infants are identified, nursing personnel are in a unique position to assist the siblings of those identified infants obtain needed medical care and services. [Copyright &y& Elsevier]
Published: 2006
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36. Parent-Child Interaction Therapy With Physically Abusive Parents: Efficacy for Reducing Future Abuse Reports.

Author: Chaffin, Mark, Silovsky, Jane F., Funderburk, Beverly, Valle, Linda Anne, Brestan, Elizabeth V., Balachova, Tatiana, Jackson, Shelli, Lensgraf, Jay, and Bonner, Barbara L.
Subjects: PARENT-child relationships, ABUSIVE parents, BEHAVIOR modification, CHILD welfare, ANTISOCIAL personality disorders, GUARDIAN & ward, SOCIAL work with children
Abstract: A randomized trial was conducted to test the efficacy and sufficiency of parent-child interaction therapy (PCIT) in preventing re-reports of physical abuse among abusive parents. Physically abusive parents (N = 110) were randomly assigned to one of three intervention conditions: (a) PCIT, (b) PCIT plus individualized enhanced services, or (c) a standard community-based parenting group. Participants had multiple past child welfare reports, severe parent-to-child violence, low household income, and significant levels of depression, substance abuse, and antisocial behavior. At a median follow-up of 850 days, 19% of parents assigned to PCIT had a re-report for physical abuse compared with 49% of parents assigned to the standard community group. Additional enhanced services did not improve the efficacy of PCIT. The relative superiority of PCIT was mediated by greater reduction in negative parent-child interactions, consistent with the PCIT change model. [ABSTRACT FROM AUTHOR]
Published: 2004
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37. The Impact of Social Change on Child Mental Health in Eastern Europe

Author: Lewis, Owen, Sargent, John, Friedrich, William, Chaffin, Mark, Cunningham, Nicholas, and Cantor, Pamela Sicher
Abstract: The transition from totalitarian to democratic societies has had both positive and negative effects on children’s mental health. Economic difficulties brought a decline in services that formerly protected children and families in certain aspects. Positive changes include a movement toward democratic process within the family, depoliticization of mental illness, passage of laws ensuring basic children’s rights, increased awareness about child abuse, and proliferation of mental health and support centers.
Published: 2001
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38. Abstract 13271: Combined Assessments of Monogenic and Polygenic Risk for Dilated Cardiomyopathy

Author: Aragam, Krishna G, Biddinger, Kiran, Jurgens, Sean J, Wang, Minxian, Pirruccello, James, Maamari, Dimitri, Chaffin, Mark, Choi, Seung H, Morrill, Valerie, Newton-cheh, Christopher, Khera, Amit V, Lubitz, Steven A, and Ellinor, Patrick T
Abstract: Introduction:Genetic testing for non-ischemic dilated cardiomyopathy (DCM) currently comprises assessment of rare (monogenic) mutations in DCM-relevant genes. The role of a common-variant (polygenic) predictor for DCM has yet to be evaluated.Methods:Leveraging genotyping array and exome-sequencing data from UK Biobank, we curated monogenic, predicted loss-of-function (LOF) mutations in 91 genes on clinical genetic testing panels for DCM, and tested their prevalence and penetrance. Next, we constructed and evaluated a polygenic score for DCM from measures of left ventricular structure and function by cardiac magnetic resonance imaging. Finally, we combined monogenic carrier status with the polygenic score to estimate aggregate genetic risk for DCM.Results:Among study participants (N= 166,690), mean age was 57 (SD, 8.1) years, 75,154 (41%) were male, and 336 (0.21%) had DCM. 14.4% of DCM cases harbored a disease-relevant LOF mutation, including 8.8% within cardiac exons of the sarcomeric gene TTN. LOF mutations in TTN(OR, 25.36), DSP(OR, 17.55), and RBM20(OR, 12.85) were the most strongly associated with dilated cardiomyopathy (all P< 0.001). 0.7% of all LOF carriers - and 4.0% of LOF carriers in TTN- developed DCM. A polygenic score comprising 2.3 million common genetic variants was strongly associated with DCM in all participants (OR per SD=1.62, p=8.61E-39), and in non-carriers (OR per SD=1.60, p=6.28E-15; N=159,811) and carriers (OR per SD=1.79, p=4.76E-05; N=6,879) of LOF mutations. Comparing the top and bottom 20% of the polygenic score, a >3-fold risk difference was observed among non-carriers of TTNLOF, and a >10-fold risk difference among TTNLOF carriers. TTNLOF carriers with high polygenic risk were at 46-fold odds of developing DCM as compared to non-carriers with intermediate polygenic risk (p=4.16E-29).Conclusions:Combining monogenic and polygenic risk expands the scope and improves the precision of clinical genetic testing for DCM.
Published: 2021
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39. False Negatives in Sexual Abuse Disclosure Interviews: Incidence and Influence of Caretaker's Belief in Abuse in Cases of Accidental Abuse Discovery by Diagnosis of STD

Author: LAWSON, LOUANNE and CHAFFIN, MARK
Abstract: Verbal disclosure of abuse in a specialized interview was studied in a sample of 28 children, ages 3 to menarche, who presented with purely physical complaints later diagnosed as a sexually transmitted disease, in the absence of any known prior disclosure or suspicion of sexual abuse. Only 43% gave any verbal confirmation of sexual contact. Fifty-seven percent were “false negatives.” Disclosure was strongly associated with the attitude taken by the child's caretaker toward the possibility of abuse. Children whose caretakers accepted the possibility that their child might have been sexually abused disclosed at a rate almost 3.5 times as great as those whose caretakers denied any possibility of abuse (63% vs. 17%). The results suggest that caretaker attitude and support is a critical variable in the child's disclosure process and a valuable target for intervention and prevention efforts. In addition, it was found that, aside from their STD, many of these abused children presented as free from any specifically suspicious abuse symptoms, suggesting that reliance on single interviews and identification of “red flags” cannot be expected to identify many hidden victims.
Published: 1992
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40. Internalizing and externalizing characteristics of sexually and/or physically abused children

Author: Dykman, Roscoe, McPherson, Brian, Ackerman, Peggy, Newton, Joseph, Mooney, Donna, Wherry, Jeff, and Chaffin, Mark
Abstract: Abstract: This study evaluates the behavioral consequences of childhood abuse (sexual, physical, or both), with particular focus on prevalence of posttraumatic stress disorder (PTSD). Three abuse type groups and nonabused controls were contrasted on behavioral rating scales and on structured psychiatric interview data. The participants (109 abused children and 16 normal control children) were recruited from Arkansas Children’s Hospital and local agencies for abused children. As expected, proportionately more females than males were sexually abused. Overall, males were rated as more disturbed than females. Type of abuse did not consistently influence behavioral ratings. Externalizing scores were significantly higher than internalizing scores in all abused groups. PTSD was diagnosed in 50% of the abused children, with a higher rate for boys who had been sexually abused as opposed to physically abused only (58% versus 13%). The most frequent comorbid condition with PTSD was Separation Anxiety. Sexually abused boys were hospitalized for psychiatric treatment at a higher rate than were other abused children.
Published: 1997
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41. Response by Aragam et al to Letter Regarding Article, “Phenotypic Refinement of Heart Failure in a National Biobank Facilitates Genetic Discovery”

Author: Aragam, Krishna G., Chaffin, Mark, Ellinor, Patrick T., Kathiresan, Sekar, and Lubitz, Steven A.
Published: 2019
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42. Abstract 10573: Epigenetic Analyses of Human Left Atrial Tissue Identifies Gene Networks Underlying Atrial Fibrillation

Author: Hall, Amelia W, Tucker, Nathan R, Chaffin, Mark, Roselli, Carolina, Lin, Honghuang, Bianchi, Valerio, Geeven, Geert, Bedi, Kenneth C, Margulies, Kenneth, De Laat, Wouter, and Ellinor, Patrick T
Abstract: Introduction:Atrial fibrillation (AF) often arises from dysfunction in the left atrium (LA), a tissue with limited annotation of the non-coding genome and its effects on gene expression. As many AF-associated genetic variants reside in these regions, this knowledge gap hinders efforts to understand AF biology.Methods:In order to generate a model of the LA non-coding genome, we profiled 7 histone post-translational modifications (active: H3K4me3, H3K4me2, H3K4me1, H3K27ac, H3K36me3; repressive: H3K27me3, H3K9me3), CTCFbinding, and gene expression in 4 LA samples from individuals without structural heart disease or AF. We used MACS2 to identify peak regions (P < 0.01), applied a Markov model approach to classify regulatory elements, and then annotated with gene expression. In order to better relate this model with disease biology, we then intersected chromatin states with independently derived eQTL, DNA methylation, and HiC long-range chromatin interaction data from primary human LA and left ventricle.Results:Our model identified 21 states, which recapitulate regulatory motifs such as promoters, enhancers, and repressed regions. Genes were actively regulated by proximal chromatin states, and repressive states was associated with a significant reduction in gene expression (P < 2x10-16). Further, chromatin states were differentially methylated, with promoters being less methylated than repressed regions (P < 2x10-16). For enhancer-gene associations, we identified 142 significantly enriched pathways (P < 0.05; FDR). Intersecting AF, PR, and QT SNP loci with cell type specific promoters identified a gene interaction network dominated by CAV1, CDKN1A, CREBP, NKX2-5and SYNPO2L.Discussion:Our profiling of the non-coding genome in human LA provides new insights into the gene expression and chromatin regulation within this AF-associated tissue. Further analyses of these data will be used to inform the relationship between GWAS variants and AF pathology.
Published: 2019
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43. Abstract 15044: Risk of Myocardial Infarction in Carriers of Familial Hypercholesterolemia Mutations is Modified by Common Variant Genetic Background or Adherence to a Healthy Lifestyle

Author: Fahed, Akl C, Wang, Minxian, Chaffin, Mark, Bick, Alexander G, Patterson, Candace, Natarajan, Pradeep, Lebo, Matthew, Batra, Puneet, Ng, Kenney, Ellinor, Patrick T, Philippakis, Anthony A, Kathiresan, Sekar, and Khera, Amit V
Abstract: Background:Recent genetic studies have highlighted ?incomplete penetrance? of DNA sequence variants related to familial hypercholesterolemia mutations - although carriers on average have a three-fold increased risk for myocardial infarction (MI), many remain healthy.Objective:We determine the extent to which a favorable genetic background or adherence to a healthy lifestyle modify the penetrance of FH mutations.Methods:Sequencing of the three genes causal for FH (LDLR, APOB, and PCSK9) was performed in 6449 cases with MI and 6430 controls from the UK Biobank. Common variant genetic background was assessed using a recently validated genome-wide polygenic score (GPS). We also determined adherence to a healthy lifestyle using a scoring system consisting of 5 factors: no current smoking, no overweight, regular physical activity, low sedentary time, and a healthy diet.Results:An FH mutation was identified in 74 of 6449 (1.15%) MI cases and 25 of 6430 (0.39%) controls. On average, mutation carriers had a 40.3 (95%CI 36.7-43.9) mg/dL higher LDL cholesterol and a 2.9 (95%CI 1.7-4.2)-fold increased risk for MI. However, FH mutation carriers with a favorable common variant background - defined as the lowest quintile of the GPS - had risk comparable to that of noncarriers, odds ratio 1.1 (95%CI 0.5-2.8). In contrast, FH mutation carriers with an unfavorable common variant background - defined as the highest quintile of GPS - had an exaggerated risk, odds ratio 5.0 (95% CI 3.4-6.5). A favorable lifestyle - defined as at least 3 of the 5 healthy lifestyle factors - also modified the impact of an FH mutation, odds ratio of 1.6 (95%CI 0.1-3.1).Conclusions:Both a favorable common variant genetic background and adherence to a healthy lifestyle offset the risk of myocardial infarction among carriers of an FH mutation. A comprehensive risk model that integrates genetic and nongenetic factors is likely to prove useful in risk stratification and counseling of FH mutation carriers.
Published: 2019
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44. Abstract 13350: Genome-Wide Association Analysis of Cardiac Magnetic Resonance Imaging in >24,000 Participants Reveals 24 Novel Loci and Shared Genetic Basis With Cardiomyopathies

Author: Pirruccello, James P, Wang, Minxian, Chaffin, Mark, Kathiresan, Sekar, and Aragam, Krishna
Abstract: Introduction:Common genetic factors that contribute to variation in the size, function, and disease propensity of the heart remain under-explored.Hypothesis:This study sought to discover common genetic polymorphisms associated with phenotypes derived from cardiac magnetic resonance imaging (MRI) in the general population, and to determine whether those polymorphisms associated with cardiomyopathy.Methods:We performed epidemiologic analyses and a genome-wide association study (GWAS) of cardiac MRI-derived left ventricular ejection fraction and volumetric measurements in 24,474 participants without heart failure or coronary artery disease from the UK Biobank. We then evaluated the association of identified variants with clinical nonischemic dilated cardiomyopathy (DCM) in the remainder of the UK Biobank.Results:Epidemiological factors, including alcohol intake and physical activity, correlated with cardiac MRI phenotypes, and explained between 8.7% and 49.4% of their variability. 34 genetic loci?including 24 novel loci?were associated with left ventricular phenotypes at genome-wide significance (P < 510-8). Eight loci were near genes previously shown to cause Mendelian cardiac diseases, including BAG3and TTN. A polygenic score derived from the 13 lead variants associated with left ventricular end systolic volume was associated with incident DCM (hazard ratio = 1.39 per one standard deviation increase in the polygenic score, P = 9.710-11).Conclusions:We identified 24 novel genetic loci associated with cardiac MRI-derived measures of left ventricular structure and function in the general population. Several common genetic variants that associated with normal variation in left ventricular structure and function may also confer risk for DCM.
Published: 2019
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45. Abstract 16565: Integration Of A Genome-wide Polygenic Score With ACC/AHA Pooled Cohorts Equation In Prediction Of Coronary Artery Disease Events In >285,000 Participants

Author: Hindy, George, Aragam, Krishna, Chaffin, Mark, Orho-Melander, Marju, Melander, Olle, Khera, Amit V, and Kathiresan, Sekar
Abstract: Genome-wide polygenic scores for coronary artery disease (GPSCAD) integrate information from millions of common DNA polymorphisms into a single measure of inherited susceptibility. The incremental value of adding GPSCADto the ACC/AHA Pooled Cohorts Equation (PCE) in predicting incident events remains uncertain. In two prospective cohort studies - the Malm? Diet and Cancer Study (MDCS) and UK Biobank (UKB), we tested the predictive performance of GPSCADwhen integrated with PCE. MDCS included 5,660 individuals (41% men, 57 years mean age) of whom 812 developed CAD during a median follow-up of 23.2 years and the UKB included 280,265 individuals (44% men, 57 years mean age) of whom 6,727 developed CAD during a median follow-up of 8.1 years. We compared the improvement in the C-statistic for GPSCADon top of the 10-year atherosclerotic cardiovascular disease (ASCVD) risk score as estimated by PCE. We obtained absolute CAD event rates in strata of genetic risk categories (low=bottom quintile; intermediate=middle 60%; high=top quintile) and clinical risk categories defined by the PCE 10-year risk (low <5%; borderline 5-7.5%; intermediate 7.5-20%; high >20%). The GPSCADshowed low correlation with PCE in both MDCS (r=0.03, P=0.06) and UKB (r=0.01, P=0.01). In multivariable models in UKB, for every standard deviation change in GPSCAD, the HR for incident CAD was 1.47 (P=1?10-172), an effect size which was higher than the risk conferred by total cholesterol (HR=1.24 per standard deviation change). In UKB, C-statistics for models with GPSCADalone, GPSCADwith age/sex, PCE alone, and PCE + GPSCADwere respectively 0.609?0.01, 0.754?0.01, 0.755?0.01, 0.764?0.01, respectively. In both MDCS and UKB, within each PCE category, GPSCADadded incremental information (Figure). GPSCAD, a risk number calculable early in life, is a strong predictor of incident CAD and provides predictive information beyond that given by standard risk factors.
Published: 2019
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46. Rare Protein-Truncating Variants in APOB, Lower Low-Density Lipoprotein Cholesterol, and Protection Against Coronary Heart Disease

Author: Peloso, Gina M., Nomura, Akihiro, Khera, Amit V., Chaffin, Mark, Won, Hong-Hee, Ardissino, Diego, Danesh, John, Schunkert, Heribert, Wilson, James G., Samani, Nilesh, Erdmann, Jeanette, McPherson, Ruth, Watkins, Hugh, Saleheen, Danish, McCarthy, Shane, Teslovich, Tanya M., Leader, Joseph B., Lester Kirchner, H., Marrugat, Jaume, Nohara, Atsushi, Kawashiri, Masa-aki, Tada, Hayato, Dewey, Frederick E., Carey, David J., Baras, Aris, and Kathiresan, Sekar
Abstract: Supplemental Digital Content is available in the text.
Published: 2019
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47. Author Correction: A genome-wide cross-trait analysis from UK Biobank highlights the shared genetic architecture of asthma and allergic diseases

Author: Zhu, Zhaozhong, Lee, Phil H., Chaffin, Mark D., Chung, Wonil, Loh, Po-Ru, Lu, Quan, Christiani, David C., and Liang, Liming
Abstract: In the version of this article originally published, there were two errors in the text of the second paragraph of the Methods section. In the sentence “To identify genetic variants that contribute to doctor-diagnosed asthma and allergic diseases (detailed phenotype information described in the Supplementary Note) and link them with other conditions, we performed GWASs using phenotype measures in UK Biobank participants (N= 487,409)” the number of participants should have been 150,509. In the sentence “Thus, a total of 110,361 European descendants with high-quality genotyping and complete phenotype/covariate data were used for these analyses, including 25,685 allergic diseases subjects (hay fever/allergic rhinitis or eczema, without doctor-diagnosed asthma), 14,085 asthma subjects and 76,768 controls for the analysis” the phrase “without doctor-diagnosed asthma” should have read “some with doctor-diagnosed asthma.” The errors have been corrected in the HTML and PDF versions of the article.
Published: 2018
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48. A screening and brief intervention for women in OB/GYN care

Author: Balachova, Tatiana, Chaffin, Mark, Bonner, Barbara, Isurina, Galina, Tsvetkova, Larissa, and Volkova, Elena
Published: 2015
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49. Heritability of Atrial Fibrillation

Author: Weng, Lu-Chen, Choi, Seung Hoan, Klarin, Derek, Smith, J. Gustav, Loh, Po-Ru, Chaffin, Mark, Roselli, Carolina, Hulme, Olivia L., Lunetta, Kathryn L., Dupuis, Josée, Benjamin, Emelia J., Newton-Cheh, Christopher, Kathiresan, Sekar, Ellinor, Patrick T., and Lubitz, Steven A.
Abstract: Supplemental Digital Content is available in the text.
Published: 2017
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50. Erratum: Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation

Author: Christophersen, Ingrid E, Rienstra, Michiel, Roselli, Carolina, Yin, Xiaoyan, Geelhoed, Bastiaan, Barnard, John, Lin, Honghuang, Arking, Dan E, Smith, Albert V, Albert, Christine M, Chaffin, Mark, Tucker, Nathan R, Li, Molong, Klarin, Derek, Bihlmeyer, Nathan A, Low, Siew-Kee, Weeke, Peter E, Müller-Nurasyid, Martina, Smith, J Gustav, Brody, Jennifer A, Niemeijer, Maartje N, Dörr, Marcus, Trompet, Stella, Huffman, Jennifer, Gustafsson, Stefan, Schurmann, Claudia, Kleber, Marcus E, Lyytikäinen, Leo-Pekka, Seppälä, Ilkka, Malik, Rainer, R V R Horimoto, Andrea, Perez, Marco, Sinisalo, Juha, Aeschbacher, Stefanie, Thériault, Sébastien, Yao, Jie, Radmanesh, Farid, Weiss, Stefan, Teumer, Alexander, Choi, Seung Hoan, Weng, Lu-Chen, Clauss, Sebastian, Deo, Rajat, Rader, Daniel J, Shah, Svati H, Sun, Albert, Hopewell, Jemma C, Debette, Stephanie, Chauhan, Ganesh, Yang, Qiong, Worrall, Bradford B, Paré, Guillaume, Kamatani, Yoichiro, Hagemeijer, Yanick P, Verweij, Niek, Siland, Joylene E, Kubo, Michiaki, Smith, Jonathan D, Van Wagoner, David R, Bis, Joshua C, Perz, Siegfried, Psaty, Bruce M, Ridker, Paul M, Magnani, Jared W, Harris, Tamara B, Launer, Lenore J, Shoemaker, M Benjamin, Padmanabhan, Sandosh, Haessler, Jeffrey, Bartz, Traci M, Waldenberger, Melanie, Lichtner, Peter, Arendt, Marina, Krieger, Jose E, Kähönen, Mika, Risch, Lorenz, Mansur, Alfredo J, Peters, Annette, Smith, Blair H, Lind, Lars, Scott, Stuart A, Lu, Yingchang, Bottinger, Erwin B, Hernesniemi, Jussi, Lindgren, Cecilia M, Wong, Jorge A, Huang, Jie, Eskola, Markku, Morris, Andrew P, Ford, Ian, Reiner, Alex P, Delgado, Graciela, Chen, Lin Y, Chen, Yii-Der Ida, Sandhu, Roopinder K, Li, Man, Boerwinkle, Eric, Eisele, Lewin, Lannfelt, Lars, Rost, Natalia, Anderson, Christopher D, Taylor, Kent D, Campbell, Archie, Magnusson, Patrik K, Porteous, David, Hocking, Lynne J, Vlachopoulou, Efthymia, Pedersen, Nancy L, Nikus, Kjell, Orho-Melander, Marju, Hamsten, Anders, Heeringa, Jan, Denny, Joshua C, Kriebel, Jennifer, Darbar, Dawood, Newton-Cheh, Christopher, Shaffer, Christian, Macfarlane, Peter W, Heilmann-Heimbach, Stefanie, Almgren, Peter, Huang, Paul L, Sotoodehnia, Nona, Soliman, Elsayed Z, Uitterlinden, Andre G, Hofman, Albert, Franco, Oscar H, Völker, Uwe, Jöckel, Karl-Heinz, Sinner, Moritz F, Lin, Henry J, Guo, Xiuqing, Dichgans, Martin, Ingelsson, Erik, Kooperberg, Charles, Melander, Olle, J F Loos, Ruth, Laurikka, Jari, Conen, David, Rosand, Jonathan, van der Harst, Pim, Lokki, Marja-Liisa, Kathiresan, Sekar, Pereira, Alexandre, Jukema, J Wouter, Hayward, Caroline, Rotter, Jerome I, März, Winfried, Lehtimäki, Terho, Stricker, Bruno H, Chung, Mina K, Felix, Stephan B, Gudnason, Vilmundur, Alonso, Alvaro, Roden, Dan M, Kääb, Stefan, Chasman, Daniel I, Heckbert, Susan R, Benjamin, Emelia J, Tanaka, Toshihiro, Lunetta, Kathryn L, Lubitz, Steven A, and Ellinor, Patrick T
Published: 2017
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