Browsing by Author "Psaty, Bruce M"

Now showing 1 - 8 of 8

Open 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.
Open Access
A meta-analysis of four genome-wide association studies of survival to age 90 years or older: the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium.
(J Gerontol A Biol Sci Med Sci, 2010-05) Newman, Anne B; Walter, Stefan; Lunetta, Kathryn L; Garcia, Melissa E; Slagboom, P Eline; Christensen, Kaare; Arnold, Alice M; Aspelund, Thor; Aulchenko, Yurii S; Benjamin, Emelia J; Christiansen, Lene; D'Agostino, Ralph B; Fitzpatrick, Annette L; Franceschini, Nora; Glazer, Nicole L; Gudnason, Vilmundur; Hofman, Albert; Kaplan, Robert; Karasik, David; Kelly-Hayes, Margaret; Kiel, Douglas P; Launer, Lenore J; Marciante, Kristin D; Massaro, Joseph M; Miljkovic, Iva; Nalls, Michael A; Hernandez, Dena; Psaty, Bruce M; Rivadeneira, Fernando; Rotter, Jerome; Seshadri, Sudha; Smith, Albert V; Taylor, Kent D; Tiemeier, Henning; Uh, Hae-Won; Uitterlinden, André G; Vaupel, James W; Walston, Jeremy; Westendorp, Rudi GJ; Harris, Tamara B; Lumley, Thomas; van Duijn, Cornelia M; Murabito, Joanne M
BACKGROUND: Genome-wide association studies (GWAS) may yield insights into longevity. METHODS: We performed a meta-analysis of GWAS in Caucasians from four prospective cohort studies: the Age, Gene/Environment Susceptibility-Reykjavik Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Longevity was defined as survival to age 90 years or older (n = 1,836); the comparison group comprised cohort members who died between the ages of 55 and 80 years (n = 1,955). In a second discovery stage, additional genotyping was conducted in the Leiden Longevity Study cohort and the Danish 1905 cohort. RESULTS: There were 273 single-nucleotide polymorphism (SNP) associations with p < .0001, but none reached the prespecified significance level of 5 x 10(-8). Of the most significant SNPs, 24 were independent signals, and 16 of these SNPs were successfully genotyped in the second discovery stage, with one association for rs9664222, reaching 6.77 x 10(-7) for the combined meta-analysis of CHARGE and the stage 2 cohorts. The SNP lies in a region near MINPP1 (chromosome 10), a well-conserved gene involved in regulation of cellular proliferation. The minor allele was associated with lower odds of survival past age 90 (odds ratio = 0.82). Associations of interest in a homologue of the longevity assurance gene (LASS3) and PAPPA2 were not strengthened in the second stage. CONCLUSION: Survival studies of larger size or more extreme or specific phenotypes may support or refine these initial findings.
Open Access
Blood Pressure Trajectory, Gait Speed, and Outcomes: The Health, Aging, and Body Composition Study
(The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 2016-12) Odden, Michelle C; Wu, Chenkai; Shlipak, Michael G; Psaty, Bruce M; Katz, Ronit; Applegate, William B; Harris, Tamara; Newman, Anne B; Peralta, Carmen A; Health ABC Study
Open Access
C-reactive protein, fibrinogen, and cardiovascular disease prediction.
(The New England journal of medicine, 2012-10) Emerging Risk Factors Collaboration; Kaptoge, Stephen; Di Angelantonio, Emanuele; Pennells, Lisa; Wood, Angela M; White, Ian R; Gao, Pei; Walker, Matthew; Thompson, Alexander; Sarwar, Nadeem; Caslake, Muriel; Butterworth, Adam S; Amouyel, Philippe; Assmann, Gerd; Bakker, Stephan JL; Barr, Elizabeth LM; Barrett-Connor, Elizabeth; Benjamin, Emelia J; Björkelund, Cecilia; Brenner, Hermann; Brunner, Eric; Clarke, Robert; Cooper, Jackie A; Cremer, Peter; Cushman, Mary; Dagenais, Gilles R; D'Agostino, Ralph B; Dankner, Rachel; Davey-Smith, George; Deeg, Dorly; Dekker, Jacqueline M; Engström, Gunnar; Folsom, Aaron R; Fowkes, F Gerry R; Gallacher, John; Gaziano, J Michael; Giampaoli, Simona; Gillum, Richard F; Hofman, Albert; Howard, Barbara V; Ingelsson, Erik; Iso, Hiroyasu; Jørgensen, Torben; Kiechl, Stefan; Kitamura, Akihiko; Kiyohara, Yutaka; Koenig, Wolfgang; Kromhout, Daan; Kuller, Lewis H; Lawlor, Debbie A; Meade, Tom W; Nissinen, Aulikki; Nordestgaard, Børge G; Onat, Altan; Panagiotakos, Demosthenes B; Psaty, Bruce M; Rodriguez, Beatriz; Rosengren, Annika; Salomaa, Veikko; Kauhanen, Jussi; Salonen, Jukka T; Shaffer, Jonathan A; Shea, Steven; Ford, Ian; Stehouwer, Coen DA; Strandberg, Timo E; Tipping, Robert W; Tosetto, Alberto; Wassertheil-Smoller, Sylvia; Wennberg, Patrik; Westendorp, Rudi G; Whincup, Peter H; Wilhelmsen, Lars; Woodward, Mark; Lowe, Gordon DO; Wareham, Nicholas J; Khaw, Kay-Tee; Sattar, Naveed; Packard, Chris J; Gudnason, Vilmundur; Ridker, Paul M; Pepys, Mark B; Thompson, Simon G; Danesh, John
There is debate about the value of assessing levels of C-reactive protein (CRP) and other biomarkers of inflammation for the prediction of first cardiovascular events.We analyzed data from 52 prospective studies that included 246,669 participants without a history of cardiovascular disease to investigate the value of adding CRP or fibrinogen levels to conventional risk factors for the prediction of cardiovascular risk. We calculated measures of discrimination and reclassification during follow-up and modeled the clinical implications of initiation of statin therapy after the assessment of CRP or fibrinogen.The addition of information on high-density lipoprotein cholesterol to a prognostic model for cardiovascular disease that included age, sex, smoking status, blood pressure, history of diabetes, and total cholesterol level increased the C-index, a measure of risk discrimination, by 0.0050. The further addition to this model of information on CRP or fibrinogen increased the C-index by 0.0039 and 0.0027, respectively (P<0.001), and yielded a net reclassification improvement of 1.52% and 0.83%, respectively, for the predicted 10-year risk categories of "low" (<10%), "intermediate" (10% to <20%), and "high" (≥20%) (P<0.02 for both comparisons). We estimated that among 100,000 adults 40 years of age or older, 15,025 persons would initially be classified as being at intermediate risk for a cardiovascular event if conventional risk factors alone were used to calculate risk. Assuming that statin therapy would be initiated in accordance with Adult Treatment Panel III guidelines (i.e., for persons with a predicted risk of ≥20% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), additional targeted assessment of CRP or fibrinogen levels in the 13,199 remaining participants at intermediate risk could help prevent approximately 30 additional cardiovascular events over the course of 10 years.In a study of people without known cardiovascular disease, we estimated that under current treatment guidelines, assessment of the CRP or fibrinogen level in people at intermediate risk for a cardiovascular event could help prevent one additional event over a period of 10 years for every 400 to 500 people screened. (Funded by the British Heart Foundation and others.).
Open Access
Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.
(European heart journal, 2019-02) Pennells, Lisa; Kaptoge, Stephen; Wood, Angela; Sweeting, Mike; Zhao, Xiaohui; White, Ian; Burgess, Stephen; Willeit, Peter; Bolton, Thomas; Moons, Karel GM; van der Schouw, Yvonne T; Selmer, Randi; Khaw, Kay-Tee; Gudnason, Vilmundur; Assmann, Gerd; Amouyel, Philippe; Salomaa, Veikko; Kivimaki, Mika; Nordestgaard, Børge G; Blaha, Michael J; Kuller, Lewis H; Brenner, Hermann; Gillum, Richard F; Meisinger, Christa; Ford, Ian; Knuiman, Matthew W; Rosengren, Annika; Lawlor, Debbie A; Völzke, Henry; Cooper, Cyrus; Marín Ibañez, Alejandro; Casiglia, Edoardo; Kauhanen, Jussi; Cooper, Jackie A; Rodriguez, Beatriz; Sundström, Johan; Barrett-Connor, Elizabeth; Dankner, Rachel; Nietert, Paul J; Davidson, Karina W; Wallace, Robert B; Blazer, Dan G; Björkelund, Cecilia; Donfrancesco, Chiara; Krumholz, Harlan M; Nissinen, Aulikki; Davis, Barry R; Coady, Sean; Whincup, Peter H; Jørgensen, Torben; Ducimetiere, Pierre; Trevisan, Maurizio; Engström, Gunnar; Crespo, Carlos J; Meade, Tom W; Visser, Marjolein; Kromhout, Daan; Kiechl, Stefan; Daimon, Makoto; Price, Jackie F; Gómez de la Cámara, Agustin; Wouter Jukema, J; Lamarche, Benoît; Onat, Altan; Simons, Leon A; Kavousi, Maryam; Ben-Shlomo, Yoav; Gallacher, John; Dekker, Jacqueline M; Arima, Hisatomi; Shara, Nawar; Tipping, Robert W; Roussel, Ronan; Brunner, Eric J; Koenig, Wolfgang; Sakurai, Masaru; Pavlovic, Jelena; Gansevoort, Ron T; Nagel, Dorothea; Goldbourt, Uri; Barr, Elizabeth LM; Palmieri, Luigi; Njølstad, Inger; Sato, Shinichi; Monique Verschuren, WM; Varghese, Cherian V; Graham, Ian; Onuma, Oyere; Greenland, Philip; Woodward, Mark; Ezzati, Majid; Psaty, Bruce M; Sattar, Naveed; Jackson, Rod; Ridker, Paul M; Cook, Nancy R; D'Agostino, Ralph B; Thompson, Simon G; Danesh, John; Di Angelantonio, Emanuele; Emerging Risk Factors Collaboration
AIMS:There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after 'recalibration', a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied. METHODS AND RESULTS:Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at 'high' 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29-39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22-24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms. CONCLUSION:Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.
Open 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.
Open Access
Visit-to-Visit Blood Pressure Variability and Mortality and Cardiovascular Outcomes Among Older Adults: The Health, Aging, and Body Composition Study
(American Journal of Hypertension, 2017-02) Wu, Chenkai; Shlipak, Michael G; Stawski, Robert S; Peralta, Carmen A; Psaty, Bruce M; Harris, Tamara B; Satterfield, Suzanne; Shiroma, Eric J; Newman, Anne B; Odden, Michelle C; Health ABC Study
Open Access
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.