Browsing by Author "Arnett, Donna K"

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    ItemOpen Access
    A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.
    (Nature methods, 2022-12) Li, Zilin; Li, Xihao; Zhou, Hufeng; Gaynor, Sheila M; Selvaraj, Margaret Sunitha; Arapoglou, Theodore; Quick, Corbin; Liu, Yaowu; Chen, Han; Sun, Ryan; Dey, Rounak; Arnett, Donna K; Auer, Paul L; Bielak, Lawrence F; Bis, Joshua C; Blackwell, Thomas W; Blangero, John; Boerwinkle, Eric; Bowden, Donald W; Brody, Jennifer A; Cade, Brian E; Conomos, Matthew P; Correa, Adolfo; Cupples, L Adrienne; Curran, Joanne E; de Vries, Paul S; Duggirala, Ravindranath; Franceschini, Nora; Freedman, Barry I; Göring, Harald HH; Guo, Xiuqing; Kalyani, Rita R; Kooperberg, Charles; Kral, Brian G; Lange, Leslie A; Lin, Bridget M; Manichaikul, Ani; Manning, Alisa K; Martin, Lisa W; Mathias, Rasika A; Meigs, James B; Mitchell, Braxton D; Montasser, May E; Morrison, Alanna C; Naseri, Take; O'Connell, Jeffrey R; Palmer, Nicholette D; Peyser, Patricia A; Psaty, Bruce M; Raffield, Laura M; Redline, Susan; Reiner, Alexander P; Reupena, Muagututi'a Sefuiva; Rice, Kenneth M; Rich, Stephen S; Smith, Jennifer A; Taylor, Kent D; Taub, Margaret A; Vasan, Ramachandran S; Weeks, Daniel E; Wilson, James G; Yanek, Lisa R; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Lipids Working Group; Rotter, Jerome I; Willer, Cristen J; Natarajan, Pradeep; Peloso, Gina M; Lin, Xihong
    Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.
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    ItemOpen Access
    Epigenome-wide association study of kidney function identifies trans-ethnic and ethnic-specific loci.
    (Genome medicine, 2021-04-30) Breeze, Charles E; Batorsky, Anna; Lee, Mi Kyeong; Szeto, Mindy D; Xu, Xiaoguang; McCartney, Daniel L; Jiang, Rong; Patki, Amit; Kramer, Holly J; Eales, James M; Raffield, Laura; Lange, Leslie; Lange, Ethan; Durda, Peter; Liu, Yongmei; Tracy, Russ P; Van Den Berg, David; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed MESA Multi-Omics Working Group; Evans, Kathryn L; Kraus, William E; Shah, Svati; Tiwari, Hermant K; Hou, Lifang; Whitsel, Eric A; Jiang, Xiao; Charchar, Fadi J; Baccarelli, Andrea A; Rich, Stephen S; Morris, Andrew P; Irvin, Marguerite R; Arnett, Donna K; Hauser, Elizabeth R; Rotter, Jerome I; Correa, Adolfo; Hayward, Caroline; Horvath, Steve; Marioni, Riccardo E; Tomaszewski, Maciej; Beck, Stephan; Berndt, Sonja I; London, Stephanie J; Mychaleckyj, Josyf C; Franceschini, Nora

    Background

    DNA methylation (DNAm) is associated with gene regulation and estimated glomerular filtration rate (eGFR), a measure of kidney function. Decreased eGFR is more common among US Hispanics and African Americans. The causes for this are poorly understood. We aimed to identify trans-ethnic and ethnic-specific differentially methylated positions (DMPs) associated with eGFR using an agnostic, genome-wide approach.

    Methods

    The study included up to 5428 participants from multi-ethnic studies for discovery and 8109 participants for replication. We tested the associations between whole blood DNAm and eGFR using beta values from Illumina 450K or EPIC arrays. Ethnicity-stratified analyses were performed using linear mixed models adjusting for age, sex, smoking, and study-specific and technical variables. Summary results were meta-analyzed within and across ethnicities. Findings were assessed using integrative epigenomics methods and pathway analyses.

    Results

    We identified 93 DMPs associated with eGFR at an FDR of 0.05 and replicated 13 and 1 DMPs across independent samples in trans-ethnic and African American meta-analyses, respectively. The study also validated 6 previously published DMPs. Identified DMPs showed significant overlap enrichment with DNase I hypersensitive sites in kidney tissue, sites associated with the expression of proximal genes, and transcription factor motifs and pathways associated with kidney tissue and kidney development.

    Conclusions

    We uncovered trans-ethnic and ethnic-specific DMPs associated with eGFR, including DMPs enriched in regulatory elements in kidney tissue and pathways related to kidney development. These findings shed light on epigenetic mechanisms associated with kidney function, bridging the gap between population-specific eGFR-associated DNAm and tissue-specific regulatory context.
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    ItemOpen Access
    Genetic diversity fuels gene discovery for tobacco and alcohol use.
    (Nature, 2022-12) Saunders, Gretchen RB; Wang, Xingyan; Chen, Fang; Jang, Seon-Kyeong; Liu, Mengzhen; Wang, Chen; Gao, Shuang; Jiang, Yu; Khunsriraksakul, Chachrit; Otto, Jacqueline M; Addison, Clifton; Akiyama, Masato; Albert, Christine M; Aliev, Fazil; Alonso, Alvaro; Arnett, Donna K; Ashley-Koch, Allison E; Ashrani, Aneel A; Barnes, Kathleen C; Barr, R Graham; Bartz, Traci M; Becker, Diane M; Bielak, Lawrence F; Benjamin, Emelia J; Bis, Joshua C; Bjornsdottir, Gyda; Blangero, John; Bleecker, Eugene R; Boardman, Jason D; Boerwinkle, Eric; Boomsma, Dorret I; Boorgula, Meher Preethi; Bowden, Donald W; Brody, Jennifer A; Cade, Brian E; Chasman, Daniel I; Chavan, Sameer; Chen, Yii-Der Ida; Chen, Zhengming; Cheng, Iona; Cho, Michael H; Choquet, Hélène; Cole, John W; Cornelis, Marilyn C; Cucca, Francesco; Curran, Joanne E; de Andrade, Mariza; Dick, Danielle M; Docherty, Anna R; Duggirala, Ravindranath; Eaton, Charles B; Ehringer, Marissa A; Esko, Tõnu; Faul, Jessica D; Fernandes Silva, Lilian; Fiorillo, Edoardo; Fornage, Myriam; Freedman, Barry I; Gabrielsen, Maiken E; Garrett, Melanie E; Gharib, Sina A; Gieger, Christian; Gillespie, Nathan; Glahn, David C; Gordon, Scott D; Gu, Charles C; Gu, Dongfeng; Gudbjartsson, Daniel F; Guo, Xiuqing; Haessler, Jeffrey; Hall, Michael E; Haller, Toomas; Harris, Kathleen Mullan; He, Jiang; Herd, Pamela; Hewitt, John K; Hickie, Ian; Hidalgo, Bertha; Hokanson, John E; Hopfer, Christian; Hottenga, JoukeJan; Hou, Lifang; Huang, Hongyan; Hung, Yi-Jen; Hunter, David J; Hveem, Kristian; Hwang, Shih-Jen; Hwu, Chii-Min; Iacono, William; Irvin, Marguerite R; Jee, Yon Ho; Johnson, Eric O; Joo, Yoonjung Y; Jorgenson, Eric; Justice, Anne E; Kamatani, Yoichiro; Kaplan, Robert C; Kaprio, Jaakko; Kardia, Sharon LR; Keller, Matthew C; Kelly, Tanika N; Kooperberg, Charles; Korhonen, Tellervo; Kraft, Peter; Krauter, Kenneth; Kuusisto, Johanna; Laakso, Markku; Lasky-Su, Jessica; Lee, Wen-Jane; Lee, James J; Levy, Daniel; Li, Liming; Li, Kevin; Li, Yuqing; Lin, Kuang; Lind, Penelope A; Liu, Chunyu; Lloyd-Jones, Donald M; Lutz, Sharon M; Ma, Jiantao; Mägi, Reedik; Manichaikul, Ani; Martin, Nicholas G; Mathur, Ravi; Matoba, Nana; McArdle, Patrick F; McGue, Matt; McQueen, Matthew B; Medland, Sarah E; Metspalu, Andres; Meyers, Deborah A; Millwood, Iona Y; Mitchell, Braxton D; Mohlke, Karen L; Moll, Matthew; Montasser, May E; Morrison, Alanna C; Mulas, Antonella; Nielsen, Jonas B; North, Kari E; Oelsner, Elizabeth C; Okada, Yukinori; Orrù, Valeria; Palmer, Nicholette D; Palviainen, Teemu; Pandit, Anita; Park, S Lani; Peters, Ulrike; Peters, Annette; Peyser, Patricia A; Polderman, Tinca JC; Rafaels, Nicholas; Redline, Susan; Reed, Robert M; Reiner, Alex P; Rice, John P; Rich, Stephen S; Richmond, Nicole E; Roan, Carol; Rotter, Jerome I; Rueschman, Michael N; Runarsdottir, Valgerdur; Saccone, Nancy L; Schwartz, David A; Shadyab, Aladdin H; Shi, Jingchunzi; Shringarpure, Suyash S; Sicinski, Kamil; Skogholt, Anne Heidi; Smith, Jennifer A; Smith, Nicholas L; Sotoodehnia, Nona; Stallings, Michael C; Stefansson, Hreinn; Stefansson, Kari; Stitzel, Jerry A; Sun, Xiao; Syed, Moin; Tal-Singer, Ruth; Taylor, Amy E; Taylor, Kent D; Telen, Marilyn J; Thai, Khanh K; Tiwari, Hemant; Turman, Constance; Tyrfingsson, Thorarinn; Wall, Tamara L; Walters, Robin G; Weir, David R; Weiss, Scott T; White, Wendy B; Whitfield, John B; Wiggins, Kerri L; Willemsen, Gonneke; Willer, Cristen J; Winsvold, Bendik S; Xu, Huichun; Yanek, Lisa R; Yin, Jie; Young, Kristin L; Young, Kendra A; Yu, Bing; Zhao, Wei; Zhou, Wei; Zöllner, Sebastian; Zuccolo, Luisa; 23andMe Research Team; Biobank Japan Project; Batini, Chiara; Bergen, Andrew W; Bierut, Laura J; David, Sean P; Gagliano Taliun, Sarah A; Hancock, Dana B; Jiang, Bibo; Munafò, Marcus R; Thorgeirsson, Thorgeir E; Liu, Dajiang J; Vrieze, Scott
    Tobacco and alcohol use are heritable behaviours associated with 15% and 5.3% of worldwide deaths, respectively, due largely to broad increased risk for disease and injury1-4. These substances are used across the globe, yet genome-wide association studies have focused largely on individuals of European ancestries5. Here we leveraged global genetic diversity across 3.4 million individuals from four major clines of global ancestry (approximately 21% non-European) to power the discovery and fine-mapping of genomic loci associated with tobacco and alcohol use, to inform function of these loci via ancestry-aware transcriptome-wide association studies, and to evaluate the genetic architecture and predictive power of polygenic risk within and across populations. We found that increases in sample size and genetic diversity improved locus identification and fine-mapping resolution, and that a large majority of the 3,823 associated variants (from 2,143 loci) showed consistent effect sizes across ancestry dimensions. However, polygenic risk scores developed in one ancestry performed poorly in others, highlighting the continued need to increase sample sizes of diverse ancestries to realize any potential benefit of polygenic prediction.
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    Heart disease and stroke statistics--2015 update: a report from the American Heart Association.
    (Circulation, 2015-01) Mozaffarian, Dariush; Benjamin, Emelia J; Go, Alan S; Arnett, Donna K; Blaha, Michael J; Cushman, Mary; de Ferranti, Sarah; Després, Jean-Pierre; Fullerton, Heather J; Howard, Virginia J; Huffman, Mark D; Judd, Suzanne E; Kissela, Brett M; Lackland, Daniel T; Lichtman, Judith H; Lisabeth, Lynda D; Liu, Simin; Mackey, Rachel H; Matchar, David B; McGuire, Darren K; Mohler, Emile R; Moy, Claudia S; Muntner, Paul; Mussolino, Michael E; Nasir, Khurram; Neumar, Robert W; Nichol, Graham; Palaniappan, Latha; Pandey, Dilip K; Reeves, Mathew J; Rodriguez, Carlos J; Sorlie, Paul D; Stein, Joel; Towfighi, Amytis; Turan, Tanya N; Virani, Salim S; Willey, Joshua Z; Woo, Daniel; Yeh, Robert W; Turner, Melanie B; American Heart Association Statistics Committee and Stroke Statistics Subcommittee
    Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together the most up-to-date statistics related to heart disease, stroke, and other cardiovascular and metabolic diseases and presents them in its Heart Disease and Stroke Statistical Update. The Statistical Update represents a critical resource for the lay public, policy makers, media professionals, clinicians, healthcare administrators, researchers, and others seeking the best available data on these conditions. Together, cardiovascular disease (CVD) and stroke produce immense health and economic burdens in the United States and globally. The Statistical Update brings together in a single document up-to-date information on the core health behaviors and health factors that define cardiovascular health; a range of major clinical disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, and peripheral arterial disease); and the associated outcomes (including quality of care, procedures, and economic costs). Since 2009, the annual versions of the Statistical Update have been cited >20 000 times in the literature. In 2014 alone, the various Statistical Updates were cited >5700 times. Each annual version of the Statistical Update undergoes major revisions to include the newest nationally representative data, add additional relevant published scientific findings, remove older information, add new sections or chapters, and increase the number of ways to access and use the assembled information. This year-long process, which begins as soon as the previous Statistical Update is published, is performed by the AHA Statistics Committee faculty volunteers and staff. For example, this year's edition includes a new chapter on cardiac arrest, new data on the monitoring and benefits of cardiovascular health in the population, additional information in many chapters on the global CVD and stroke burden, and further new focus on evidence- based approaches to changing behaviors, implementation strategies, and implications of the AHA's 2020 Impact Goals. Below are a few highlights from this year's Update.
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    ItemOpen Access
    Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.
    (Nature genetics, 2023-01) Li, Xihao; Quick, Corbin; Zhou, Hufeng; Gaynor, Sheila M; Liu, Yaowu; Chen, Han; Selvaraj, Margaret Sunitha; Sun, Ryan; Dey, Rounak; Arnett, Donna K; Bielak, Lawrence F; Bis, Joshua C; Blangero, John; Boerwinkle, Eric; Bowden, Donald W; Brody, Jennifer A; Cade, Brian E; Correa, Adolfo; Cupples, L Adrienne; Curran, Joanne E; de Vries, Paul S; Duggirala, Ravindranath; Freedman, Barry I; Göring, Harald HH; Guo, Xiuqing; Haessler, Jeffrey; Kalyani, Rita R; Kooperberg, Charles; Kral, Brian G; Lange, Leslie A; Manichaikul, Ani; Martin, Lisa W; McGarvey, Stephen T; Mitchell, Braxton D; Montasser, May E; Morrison, Alanna C; Naseri, Take; O'Connell, Jeffrey R; Palmer, Nicholette D; Peyser, Patricia A; Psaty, Bruce M; Raffield, Laura M; Redline, Susan; Reiner, Alexander P; Reupena, Muagututi'a Sefuiva; Rice, Kenneth M; Rich, Stephen S; Sitlani, Colleen M; Smith, Jennifer A; Taylor, Kent D; Vasan, Ramachandran S; Willer, Cristen J; Wilson, James G; Yanek, Lisa R; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group; Rotter, Jerome I; Natarajan, Pradeep; Peloso, Gina M; Li, Zilin; Lin, Xihong
    Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.
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    ItemOpen Access
    Report of the National Heart, Lung, and Blood Institute Working Group on Hypertension: Barriers to Translation.
    (Hypertension (Dallas, Tex. : 1979), 2020-04) Sigmund, Curt D; Carey, Robert M; Appel, Lawrence J; Arnett, Donna K; Bosworth, Hayden B; Cushman, William C; Galis, Zorina S; Green Parker, Melissa; Hall, John E; Harrison, David G; McDonough, Alicia A; Nicastro, Holly L; Oparil, Suzanne; Osborn, John W; Raizada, Mohan K; Wright, Jacqueline D; Oh, Young S
    The National Heart, Lung, and Blood Institute convened a multidisciplinary working group of hypertension researchers on December 6 to 7, 2018, in Bethesda, MD, to share current scientific knowledge in hypertension and to identify barriers to translation of basic into clinical science/trials and implementation of clinical science into clinical care of patients with hypertension. The goals of the working group were (1) to provide an overview of recent discoveries that may be ready for testing in preclinical and clinical studies; (2) to identify gaps in knowledge that impede translation; (3) to highlight the most promising scientific areas in which to pursue translation; (4) to identify key challenges and barriers for moving basic science discoveries into translation, clinical studies, and trials; and (5) to identify roadblocks for effective dissemination and implementation of basic and clinical science in real-world settings. The working group addressed issues that were responsive to many of the objectives of the National Heart, Lung, and Blood Institute Strategic Vision. The working group identified major barriers and opportunities for translating research to improved control of hypertension. This review summarizes the discussion and recommendations of the working group.
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    Whole genome sequence analysis of blood lipid levels in >66,000 individuals.
    (Nature communications, 2022-10) Selvaraj, Margaret Sunitha; Li, Xihao; Li, Zilin; Pampana, Akhil; Zhang, David Y; Park, Joseph; Aslibekyan, Stella; Bis, Joshua C; Brody, Jennifer A; Cade, Brian E; Chuang, Lee-Ming; Chung, Ren-Hua; Curran, Joanne E; de Las Fuentes, Lisa; de Vries, Paul S; Duggirala, Ravindranath; Freedman, Barry I; Graff, Mariaelisa; Guo, Xiuqing; Heard-Costa, Nancy; Hidalgo, Bertha; Hwu, Chii-Min; Irvin, Marguerite R; Kelly, Tanika N; Kral, Brian G; Lange, Leslie; Li, Xiaohui; Lisa, Martin; Lubitz, Steven A; Manichaikul, Ani W; Michael, Preuss; Montasser, May E; Morrison, Alanna C; Naseri, Take; O'Connell, Jeffrey R; Palmer, Nicholette D; Palmer, Nicholette D; Peyser, Patricia A; Reupena, Muagututia S; Smith, Jennifer A; Sun, Xiao; Taylor, Kent D; Tracy, Russell P; Tsai, Michael Y; Wang, Zhe; Wang, Yuxuan; Bao, Wei; Wilkins, John T; Yanek, Lisa R; Zhao, Wei; Arnett, Donna K; Blangero, John; Boerwinkle, Eric; Bowden, Donald W; Chen, Yii-Der Ida; Correa, Adolfo; Cupples, L Adrienne; Dutcher, Susan K; Ellinor, Patrick T; Fornage, Myriam; Gabriel, Stacey; Germer, Soren; Gibbs, Richard; He, Jiang; Kaplan, Robert C; Kardia, Sharon LR; Kim, Ryan; Kooperberg, Charles; Loos, Ruth JF; Viaud-Martinez, Karine A; Mathias, Rasika A; McGarvey, Stephen T; Mitchell, Braxton D; Nickerson, Deborah; North, Kari E; Psaty, Bruce M; Redline, Susan; Reiner, Alexander P; Vasan, Ramachandran S; Rich, Stephen S; Willer, Cristen; Rotter, Jerome I; Rader, Daniel J; Lin, Xihong; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; Peloso, Gina M; Natarajan, Pradeep
    Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.