Browsing by Author "Zhao, Wei"

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
Comparing two different noise magnitude estimation methods in CT using virtual imaging trials
(Medical Imaging 2022: Physics of Medical Imaging, 2022-04-04) Ria, F; Jadick, GL; Abadi, E; Solomon, JB; Samei, E
Open Access
Costs and Benefits Associated With Transradial Versus Transfemoral Percutaneous Coronary Intervention in China.
(Journal of the American Heart Association, 2016-04-22) Jin, Chen; Li, Wei; Qiao, Shu-Bin; Yang, Jin-Gang; Wang, Yang; He, Pei-Yuan; Tang, Xin-Ran; Dong, Qiu-Ting; Li, Xiang-Dong; Yan, Hong-Bing; Wu, Yong-Jian; Chen, Ji-Lin; Gao, Run-Lin; Yuan, Jin-Qing; Dou, Ke-Fei; Xu, Bo; Zhao, Wei; Zhang, Xue; Xian, Ying; Yang, Yue-Jin
Transradial percutaneous coronary intervention (PCI) has been increasingly adopted in clinical practice, given its potential advantages over transfemoral intervention; however, the impact of different access strategies on costs and clinical outcomes remains poorly defined, especially in the developing world.Using data from a consecutive cohort of 5306 patients undergoing PCI in China in 2010, we compared total hospital costs and in-hospital outcomes for transradial intervention (TRI) and transfemoral intervention. Patients receiving TRI (n=4696, 88.5%) were slightly younger (mean age 57.4 versus 59.5 years), less often women (21.6% versus 33.1%), more likely to undergo PCI for single-vessel disease, and less likely to undergo PCI for triple-vessel or left main diseases. The unadjusted total hospital costs were 57 900 Chinese yuan (¥57 900; equivalent to 9190 US dollars [$9190]) for TRI and ¥67 418 ($10,701) for transfemoral intervention. After adjusting for all observed patient and procedural characteristics using the propensity score inverse probability weighting method, TRI was associated with a lower total cost (adjusted difference ¥8081 [$1283]). More than 80% of the cost difference was related to lower PCI-related costs (adjusted difference -¥5162 [-$819]), which were likely driven by exclusive use of vascular closure devices in transfemoral intervention, and lower hospitalization costs (-¥1399 [-$222]). Patients receiving TRI had shorter length of stay and were less likely to experience major adverse cardiac events or post-PCI bleeding. These differences were consistent among clinically relevant subgroups with acute myocardial infarction, acute coronary syndrome, and stable angina.Among patients undergoing PCI, TRI was associated with lower cost and favorable clinical outcomes compared with transfemoral intervention.
Open Access
Estimation of in vivo noise in clinical CT images: comparison and validation of three different methods against ensemble noise gold-standard
(Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115952P, 2021-02-15) Ria, Francesco; Smith, Taylor; Abadi, Ehsan; Solomon, Justin; Samei, Ehsan
Image quality estimation is crucial in modern CT with noise magnitude playing a key role. Several methods have been proposed to estimate noise surrogates in vivo. This study aimed to ascertain the accuracy of three different noise-magnitude estimation methods. We used ensemble noise as the ground truth. The most accurate approach to assess ensemble noise is to scan a patient repeatedly and assess the noise for each pixel across the ensemble of images. This process is ethically undoable on actual patients. In this study, we surmounted this impasse using Virtual Imaging Trials (VITs) that simulate clinical scenarios using computer-based simulations. XCAT phantoms were imaged 47 times using a scanner-specific simulator (DukeSim) and reconstructed with filtered back projection (FBP) and iterative (IR) algorithms. Noise magnitudes were calculated in lung (ROIn), soft tissues (GNI), and air surrounding the patient (AIRn), applying different HU thresholds and techniques. The results were compared with the ensemble noise magnitudes within soft tissue (En). For the FBP-reconstructed images, median En was 30.6 HU; median ROIn was 46.6 HU (+52%), median GNI was 40.1 HU (+31%), and median AIRn 25.1 HU (-18%). For the IR images, median En was 19.5 HU; median ROIn was 31.2 HU (+60%), median GNI was 25.1 HU (+29%), and median AIRn 18.8 HU (-4%). Compared to ensemble noise, GNI and ROIn overestimate the tissue noise, while AIRn underestimates it. Air noise was least representative of variations in tissue noise due to imaging condition. These differences may be applied as adjustment or calibration factors to better represent clinical results.
Open 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 injury^1-4. 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.
Open Access
Low frequency of paleoviral infiltration across the avian phylogeny.
(Genome Biol, 2014) Cui, Jie; Zhao, Wei; Huang, Zhiyong; Jarvis, Erich D; Gilbert, M Thomas P; Walker, Peter J; Holmes, Edward C; Zhang, Guojie
BACKGROUND: Mammalian genomes commonly harbor endogenous viral elements. Due to a lack of comparable genome-scale sequence data, far less is known about endogenous viral elements in avian species, even though their small genomes may enable important insights into the patterns and processes of endogenous viral element evolution. RESULTS: Through a systematic screening of the genomes of 48 species sampled across the avian phylogeny we reveal that birds harbor a limited number of endogenous viral elements compared to mammals, with only five viral families observed: Retroviridae, Hepadnaviridae, Bornaviridae, Circoviridae, and Parvoviridae. All nonretroviral endogenous viral elements are present at low copy numbers and in few species, with only endogenous hepadnaviruses widely distributed, although these have been purged in some cases. We also provide the first evidence for endogenous bornaviruses and circoviruses in avian genomes, although at very low copy numbers. A comparative analysis of vertebrate genomes revealed a simple linear relationship between endogenous viral element abundance and host genome size, such that the occurrence of endogenous viral elements in bird genomes is 6- to 13-fold less frequent than in mammals. CONCLUSIONS: These results reveal that avian genomes harbor relatively small numbers of endogenous viruses, particularly those derived from RNA viruses, and hence are either less susceptible to viral invasions or purge them more effectively.
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
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.