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1. Methylation patterns associated with C-reactive protein in racially and ethnically diverse populations

Author

Lundin, Jessica I, Peters, Ulrike, Hu, Yao, Ammous, Farah, Avery, Christy L, Benjamin, Emelia J, Bis, Joshua C, Brody, Jennifer A, Carlson, Chris, Cushman, Mary, Gignoux, Chris, Guo, Xiuqing, Haessler, Jeff, Haiman, Chris, Joehanes, Roby, Kasela, Silva, Kenny, Eimear, Lapalainien, Tuuli, Levy, Daniel, Liu, Chunyu, Liu, Yongmei, Loos, Ruth JF, Lu, Ake, Matise, Tara, North, Kari E, Park, Sungshim L, Ratliff, Scott M, Reiner, Alex, Rich, Stephen S, Rotter, Jerome I, Smith, Jennifer A, Sotoodehnia, Nona, Tracy, Russell, Van den Berg, David, Xu, Huichun, Ye, Ting, Zhao, Wei, Raffield, Laura M, Kooperberg, Charles, and Study, On Behalf of the PAGE

Subjects
Biological Sciences, Genetics, Minority Health, American Indian or Alaska Native, Human Genome, Health Disparities, Aetiology, 2.1 Biological and endogenous factors, Inflammatory and immune system, Humans, DNA Methylation, C-Reactive Protein, Epigenesis, Genetic, DNA, Inflammation, Genome-Wide Association Study, CpG Islands, Intracellular Signaling Peptides and Proteins, C-reactive protein, methylation, epigenetics, EWAS, racial and ethnic diversity, Mendelian randomization, causal pathway, PAGE Study, Biochemistry and Cell Biology, Medical Biochemistry and Metabolomics, Developmental Biology, Biochemistry and cell biology
Abstract

Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50% White, 41% Black or African American, 7% Hispanic or Latino/a, and 2% Native Hawaiian, Asian American, American Indian, or Alaska Native (total n = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (CADM3, NALCN, NLRC5, ZNF792, and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level (p

Published
2024

2. Influence Mechanism of the Affordances of Chronic Disease Management Apps on Continuance Intention: Questionnaire Study

Author

Liu, Yongmei, Jiang, Fei, and Lin, Peiyang

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

BackgroundMobile health apps are becoming increasingly popular, and they provide opportunities for effective health management. Existing chronic disease management (CDM) apps cannot meet users’ practical and urgent needs, and user adhesion is poor. Few studies, however, have investigated the factors that influence the continuance intention of CDM app users. ObjectiveStarting from the affordances of CDM apps, this study aimed to analyze how such apps can influence continuance intention through the role of health empowerment. MethodsAdopting a stimulus-organism-response framework, an antecedent model was established for continuance intention from the perspective of perceived affordances, uses and gratifications theory, and health empowerment. Perceived affordances were used as the “stimulus,” users’ gratifications and health empowerment were used as the “organism,” and continuance intention was used as the “response.” Data were collected online through a well-known questionnaire survey platform in China, and 323 valid questionnaires were obtained. The theoretical model was tested using structural equation modeling. ResultsPerceived connection affordances were found to have significant positive effects on social interactivity gratification (t717=6.201, P

Published
2021
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3. Identification of proteins associated with type 2 diabetes risk in diverse racial and ethnic populations

Author

Liu, Shuai, Zhu, Jingjing, Zhong, Hua, Wu, Chong, Xue, Haoran, Darst, Burcu F., Guo, Xiuqing, Durda, Peter, Tracy, Russell P., Liu, Yongmei, Johnson, W. Craig, Taylor, Kent D., Manichaikul, Ani W., Goodarzi, Mark O., Gerszten, Robert E., Clish, Clary B., Chen, Yii-Der Ida, Highland, Heather, Haiman, Christopher A., Gignoux, Christopher R., Lange, Leslie, Conti, David V., Raffield, Laura M., Wilkens, Lynne, Marchand, Loïc Le, North, Kari E., Young, Kristin L., Loos, Ruth J., Buyske, Steve, Matise, Tara, Peters, Ulrike, Kooperberg, Charles, Reiner, Alexander P., Yu, Bing, Boerwinkle, Eric, Sun, Quan, Rooney, Mary R., Echouffo-Tcheugui, Justin B., Daviglus, Martha L., Qi, Qibin, Mancuso, Nicholas, Li, Changwei, Deng, Youping, Manning, Alisa, Meigs, James B., Rich, Stephen S., Rotter, Jerome I., and Wu, Lang

Published
2024
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4. LXR signaling pathways link cholesterol metabolism with risk for prediabetes and diabetes

Author

Ding, Jingzhong, Nguyen, Anh Tram, Lohman, Kurt, Hensley, Michael T, Parker, Daniel, Hou, Li, Taylor, Jackson, Voora, Deepak, Sawyer, Janet K, Boudyguina, Elena, Bancks, Michael P, Bertoni, Alain, Pankow, James S, Rotter, Jerome I, Goodarzi, Mark O, Tracy, Russell P, Murdoch, David M, Duprez, Daniel, Rich, Stephen S, Psaty, Bruce M, Siscovick, David, Newgard, Christopher B, Herrington, David, Hoeschele, Ina, Shea, Steven, Stein, James H, Patel, Manesh, Post, Wendy, Jacobs, David, Parks, John S, and Liu, Yongmei

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Atherosclerosis, Clinical Research, Health Disparities, Obesity, Diabetes, Genetics, Nutrition, Prevention, Cardiovascular, 2.1 Biological and endogenous factors, Metabolic and endocrine, Humans, Prediabetic State, Male, Female, Diabetes Mellitus, Type 2, Middle Aged, Liver X Receptors, Cholesterol, Aged, Signal Transduction, ATP Binding Cassette Transporter, Subfamily G, Member 1, Monocytes, Risk Factors, ATP Binding Cassette Transporter 1, Aged, 80 and over, Expression profiling, Metabolism, Medical and Health Sciences, Immunology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

BACKGROUNDPreclinical studies suggest that cholesterol accumulation leads to insulin resistance. We previously reported that alterations in a monocyte cholesterol metabolism transcriptional network (CMTN) - suggestive of cellular cholesterol accumulation - were cross-sectionally associated with obesity and type 2 diabetes (T2D). Here, we sought to determine whether the CMTN alterations independently predict incident prediabetes/T2D risk, and correlate with cellular cholesterol accumulation.METHODSMonocyte mRNA expression of 11 CMTN genes was quantified among 934 Multi-Ethnic Study of Atherosclerosis (MESA) participants free of prediabetes/T2D; cellular cholesterol was measured in a subset of 24 monocyte samples.RESULTSDuring a median 6-year follow-up, lower expression of 3 highly correlated LXR target genes - ABCG1 and ABCA1 (cholesterol efflux) and MYLIP (cholesterol uptake suppression) - and not other CMTN genes, was significantly associated with higher risk of incident prediabetes/T2D. Lower expression of the LXR target genes correlated with higher cellular cholesterol levels (e.g., 47% of variance in cellular total cholesterol explained by ABCG1 expression). Further, adding the LXR target genes to overweight/obesity and other known predictors significantly improved prediction of incident prediabetes/T2D.CONCLUSIONThese data suggest that the aberrant LXR/ABCG1-ABCA1-MYLIP pathway (LAAMP) is a major T2D risk factor and support a potential role for aberrant LAAMP and cellular cholesterol accumulation in diabetogenesis.FUNDINGThe MESA Epigenomics and Transcriptomics Studies were funded by NIH grants 1R01HL101250, 1RF1AG054474, R01HL126477, R01DK101921, and R01HL135009. This work was supported by funding from NIDDK R01DK103531 and NHLBI R01HL119962.

Published
2024

5. Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits.

Author

Wittich, Henry, Ardlie, Kristin, Taylor, Kent, Durda, Peter, Liu, Yongmei, Mikhaylova, Anna, Gignoux, Chris, Cho, Michael, Rich, Stephen, Rotter, Jerome, Manichaikul, Ani, Im, Hae, and Wheeler, Heather

Subjects
TWAS, autoimmune diseases, cis-eQTL, gene expression, genetic prediction, heritability, plasma proteome, trans-eQTL, transcription factors, Humans, Transcriptome, Proteome, Multifactorial Inheritance, Quantitative Trait Loci, Genome-Wide Association Study, Polymorphism, Single Nucleotide
Abstract

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR

Published
2024

6. A methylation risk score for chronic kidney disease: a HyperGEN study

Author

Jones, Alana C, Patki, Amit, Srinivasasainagendra, Vinodh, Hidalgo, Bertha A, Tiwari, Hemant K, Limdi, Nita A, Armstrong, Nicole D, Chaudhary, Ninad S, Minniefield, Bré, Absher, Devin, Arnett, Donna K, Lange, Leslie A, Lange, Ethan M, Young, Bessie A, Diamantidis, Clarissa J, Rich, Stephen S, Mychaleckyj, Josyf C, Rotter, Jerome I, Taylor, Kent D, Kramer, Holly J, Tracy, Russell P, Durda, Peter, Kasela, Silva, Lappalinen, Tuuli, Liu, Yongmei, Johnson, W Craig, Van Den Berg, David J, Franceschini, Nora, Liu, Simin, Mouton, Charles P, Bhatti, Parveen, Horvath, Steve, Whitsel, Eric A, and Irvin, Marguerite R

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Prevention, Clinical Research, Human Genome, Health Disparities, Kidney Disease, Minority Health, Women's Health, 2.1 Biological and endogenous factors, 4.1 Discovery and preclinical testing of markers and technologies, Renal and urogenital, Humans, Renal Insufficiency, Chronic, DNA Methylation, Male, Female, Middle Aged, Glomerular Filtration Rate, CpG Islands, Risk Factors, Black or African American, Aged, Genome-Wide Association Study, Epigenesis, Genetic, Adult, Genetic Predisposition to Disease, Chronic kidney disease, eGFR, Methylation risk score, Epigenetics
Abstract

Chronic kidney disease (CKD) impacts about 1 in 7 adults in the United States, but African Americans (AAs) carry a disproportionately higher burden of disease. Epigenetic modifications, such as DNA methylation at cytosine-phosphate-guanine (CpG) sites, have been linked to kidney function and may have clinical utility in predicting the risk of CKD. Given the dynamic relationship between the epigenome, environment, and disease, AAs may be especially sensitive to environment-driven methylation alterations. Moreover, risk models incorporating CpG methylation have been shown to predict disease across multiple racial groups. In this study, we developed a methylation risk score (MRS) for CKD in cohorts of AAs. We selected nine CpG sites that were previously reported to be associated with estimated glomerular filtration rate (eGFR) in epigenome-wide association studies to construct a MRS in the Hypertension Genetic Epidemiology Network (HyperGEN). In logistic mixed models, the MRS was significantly associated with prevalent CKD and was robust to multiple sensitivity analyses, including CKD risk factors. There was modest replication in validation cohorts. In summary, we demonstrated that an eGFR-based CpG score is an independent predictor of prevalent CKD, suggesting that MRS should be further investigated for clinical utility in evaluating CKD risk and progression.

Published
2024

7. Multivariate adaptive shrinkage improves cross-population transcriptome prediction and association studies in underrepresented populations

Author

Araujo, Daniel S, Nguyen, Chris, Hu, Xiaowei, Mikhaylova, Anna V, Gignoux, Chris, Ardlie, Kristin, Taylor, Kent D, Durda, Peter, Liu, Yongmei, Papanicolaou, George, Cho, Michael H, Rich, Stephen S, Rotter, Jerome I, Consortium, NHLBI TOPMed, Im, Hae Kyung, Manichaikul, Ani, and Wheeler, Heather E

Subjects
Biological Sciences, Genetics, Human Genome, Biotechnology, Aetiology, 2.5 Research design and methodologies (aetiology), Generic health relevance, Humans, Transcriptome, Genome-Wide Association Study, Quantitative Trait Loci, Gene Frequency, Linkage Disequilibrium, NHLBI TOPMed Consortium, PrediXcan, genetics, genomics, human genetics, multi-ancestry GWAS, multivarite adaptive shrinkage, transcriptome prediction, transcriptome-wide association studies
Abstract

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized that methods that leverage shared regulatory effects across different conditions, in this case, across different populations, may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [Matrix eQTL], multivariate adaptive shrinkage in R [MASHR], and transcriptome-integrated genetic association resource [TIGAR]) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.

Published
2023

8. The functional impact of rare variation across the regulatory cascade

Author

Li, Taibo, Ferraro, Nicole, Strober, Benjamin J, Aguet, Francois, Kasela, Silva, Arvanitis, Marios, Ni, Bohan, Wiel, Laurens, Hershberg, Elliot, Ardlie, Kristin, Arking, Dan E, Beer, Rebecca L, Brody, Jennifer, Blackwell, Thomas W, Clish, Clary, Gabriel, Stacey, Gerszten, Robert, Guo, Xiuqing, Gupta, Namrata, Johnson, W Craig, Lappalainen, Tuuli, Lin, Henry J, Liu, Yongmei, Nickerson, Deborah A, Papanicolaou, George, Pritchard, Jonathan K, Qasba, Pankaj, Shojaie, Ali, Smith, Josh, Sotoodehnia, Nona, Taylor, Kent D, Tracy, Russell P, Van Den Berg, David, Wheeler, Matthew T, Rich, Stephen S, Rotter, Jerome I, Battle, Alexis, and Montgomery, Stephen B

Subjects
Biological Sciences, Health Sciences, Genetics, Human Genome, Brain Disorders, Schizophrenia, Mental Health, Aetiology, 2.1 Biological and endogenous factors, Generic health relevance, functional genomics, machine learning, methylome, multi-omics, proteome, rare variants, transcriptome
Abstract

Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.

Published
2023

9. Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma

Author

Benson, Mark D, Eisman, Aaron S, Tahir, Usman A, Katz, Daniel H, Deng, Shuliang, Ngo, Debby, Robbins, Jeremy M, Hofmann, Alissa, Shi, Xu, Zheng, Shuning, Keyes, Michelle, Yu, Zhi, Gao, Yan, Farrell, Laurie, Shen, Dongxiao, Chen, Zsu-Zsu, Cruz, Daniel E, Sims, Mario, Correa, Adolfo, Tracy, Russell P, Durda, Peter, Taylor, Kent D, Liu, Yongmei, Johnson, W Craig, Guo, Xiuqing, Yao, Jie, Chen, Yii-Der Ida, Manichaikul, Ani W, Jain, Deepti, Yang, Qiong, Consortium, NHLBI Trans-Omics for Precision Medicine, Bouchard, Claude, Sarzynski, Mark A, Rich, Stephen S, Rotter, Jerome I, Wang, Thomas J, Wilson, James G, Clish, Clary B, Sarkar, Indra Neil, Natarajan, Pradeep, and Gerszten, Robert E

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Genetics, Medical Biochemistry and Metabolomics, Biotechnology, 2.1 Biological and endogenous factors, Humans, Animals, Mice, Proteomics, Metabolomics, Signal Transduction, Genome-Wide Association Study, Polymorphism, Single Nucleotide, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, GWAS, functional genomics, genome-wide association study, genomics, metabolomics, multi-omics, pathway discovery, proteomics, Endocrinology & Metabolism, Biochemistry and cell biology, Medical biochemistry and metabolomics
Abstract

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

Published
2023

13. Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits

Author

Keaton, Jacob M., Kamali, Zoha, Xie, Tian, Vaez, Ahmad, Williams, Ariel, Goleva, Slavina B., Ani, Alireza, Evangelou, Evangelos, Hellwege, Jacklyn N., Yengo, Loic, Young, William J., Traylor, Matthew, Giri, Ayush, Zheng, Zhili, Zeng, Jian, Chasman, Daniel I., Morris, Andrew P., Caulfield, Mark J., Hwang, Shih-Jen, Kooner, Jaspal S., Conen, David, Attia, John R., Morrison, Alanna C., Loos, Ruth J. F., Kristiansson, Kati, Schmidt, Reinhold, Hicks, Andrew A., Pramstaller, Peter P., Nelson, Christopher P., Samani, Nilesh J., Risch, Lorenz, Gyllensten, Ulf, Melander, Olle, Riese, Harriette, Wilson, James F., Campbell, Harry, Rich, Stephen S., Psaty, Bruce M., Lu, Yingchang, Rotter, Jerome I., Guo, Xiuqing, Rice, Kenneth M., Vollenweider, Peter, Sundström, Johan, Langenberg, Claudia, Tobin, Martin D., Giedraitis, Vilmantas, Luan, Jian’an, Tuomilehto, Jaakko, Kutalik, Zoltan, Ripatti, Samuli, Salomaa, Veikko, Girotto, Giorgia, Trompet, Stella, Jukema, J. Wouter, van der Harst, Pim, Ridker, Paul M., Giulianini, Franco, Vitart, Veronique, Goel, Anuj, Watkins, Hugh, Harris, Sarah E., Deary, Ian J., van der Most, Peter J., Oldehinkel, Albertine J., Keavney, Bernard D., Hayward, Caroline, Campbell, Archie, Boehnke, Michael, Scott, Laura J., Boutin, Thibaud, Mamasoula, Chrysovalanto, Järvelin, Marjo-Riitta, Peters, Annette, Gieger, Christian, Lakatta, Edward G., Cucca, Francesco, Hui, Jennie, Knekt, Paul, Enroth, Stefan, De Borst, Martin H., Polašek, Ozren, Concas, Maria Pina, Catamo, Eulalia, Cocca, Massimiliano, Li-Gao, Ruifang, Hofer, Edith, Schmidt, Helena, Spedicati, Beatrice, Waldenberger, Melanie, Strachan, David P., Laan, Maris, Teumer, Alexander, Dörr, Marcus, Gudnason, Vilmundur, Cook, James P., Ruggiero, Daniela, Kolcic, Ivana, Boerwinkle, Eric, Traglia, Michela, Lehtimäki, Terho, Raitakari, Olli T., Johnson, Andrew D., Newton-Cheh, Christopher, Brown, Morris J., Dominiczak, Anna F., Sever, Peter J., Poulter, Neil, Chambers, John C., Elosua, Roberto, Siscovick, David, Esko, Tõnu, Metspalu, Andres, Strawbridge, Rona J., Laakso, Markku, Hamsten, Anders, Hottenga, Jouke-Jan, de Geus, Eco, Morris, Andrew D., Palmer, Colin N. A., Nolte, Ilja M., Milaneschi, Yuri, Marten, Jonathan, Wright, Alan, Zeggini, Eleftheria, Howson, Joanna M. M., O’Donnell, Christopher J., Spector, Tim, Nalls, Mike A., Simonsick, Eleanor M., Liu, Yongmei, van Duijn, Cornelia M., Butterworth, Adam S., Danesh, John N., Menni, Cristina, Wareham, Nicholas J., Khaw, Kay-Tee, Sun, Yan V., Wilson, Peter W. F., Cho, Kelly, Visscher, Peter M., Denny, Joshua C., Levy, Daniel, Edwards, Todd L., Munroe, Patricia B., Snieder, Harold, and Warren, Helen R.

Published
2024
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16. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Author

Suzuki, Ken, Hatzikotoulas, Konstantinos, Southam, Lorraine, Taylor, Henry J., Yin, Xianyong, Lorenz, Kim M., Mandla, Ravi, Huerta-Chagoya, Alicia, Melloni, Giorgio E. M., Kanoni, Stavroula, Rayner, Nigel W., Bocher, Ozvan, Arruda, Ana Luiza, Sonehara, Kyuto, Namba, Shinichi, Lee, Simon S. K., Preuss, Michael H., Petty, Lauren E., Schroeder, Philip, Vanderwerff, Brett, Kals, Mart, Bragg, Fiona, Lin, Kuang, Guo, Xiuqing, Zhang, Weihua, Yao, Jie, Kim, Young Jin, Graff, Mariaelisa, Takeuchi, Fumihiko, Nano, Jana, Lamri, Amel, Nakatochi, Masahiro, Moon, Sanghoon, Scott, Robert A., Cook, James P., Lee, Jung-Jin, Pan, Ian, Taliun, Daniel, Parra, Esteban J., Chai, Jin-Fang, Bielak, Lawrence F., Tabara, Yasuharu, Hai, Yang, Thorleifsson, Gudmar, Grarup, Niels, Sofer, Tamar, Wuttke, Matthias, Sarnowski, Chloé, Gieger, Christian, Nousome, Darryl, Trompet, Stella, Kwak, Soo-Heon, Long, Jirong, Sun, Meng, Tong, Lin, Chen, Wei-Min, Nongmaithem, Suraj S., Noordam, Raymond, Lim, Victor J. Y., Tam, Claudia H. T., Joo, Yoonjung Yoonie, Chen, Chien-Hsiun, Raffield, Laura M., Prins, Bram Peter, Nicolas, Aude, Yanek, Lisa R., Chen, Guanjie, Brody, Jennifer A., Kabagambe, Edmond, An, Ping, Xiang, Anny H., Choi, Hyeok Sun, Cade, Brian E., Tan, Jingyi, Broadaway, K. Alaine, Williamson, Alice, Kamali, Zoha, Cui, Jinrui, Thangam, Manonanthini, Adair, Linda S., Adeyemo, Adebowale, Aguilar-Salinas, Carlos A., Ahluwalia, Tarunveer S., Anand, Sonia S., Bertoni, Alain, Bork-Jensen, Jette, Brandslund, Ivan, Buchanan, Thomas A., Burant, Charles F., Butterworth, Adam S., Canouil, Mickaël, Chan, Juliana C. N., Chang, Li-Ching, Chee, Miao-Li, Chen, Ji, Chen, Shyh-Huei, Chen, Yuan-Tsong, Chen, Zhengming, Chuang, Lee-Ming, Cushman, Mary, Danesh, John, Das, Swapan K., de Silva, H. Janaka, Dedoussis, George, Dimitrov, Latchezar, Doumatey, Ayo P., Du, Shufa, Duan, Qing, Eckardt, Kai-Uwe, Emery, Leslie S., Evans, Daniel S., Evans, Michele K., Fischer, Krista, Floyd, James S., Ford, Ian, Franco, Oscar H., Frayling, Timothy M., Freedman, Barry I., Genter, Pauline, Gerstein, Hertzel C., Giedraitis, Vilmantas, González-Villalpando, Clicerio, González-Villalpando, Maria Elena, Gordon-Larsen, Penny, Gross, Myron, Guare, Lindsay A., Hackinger, Sophie, Hakaste, Liisa, Han, Sohee, Hattersley, Andrew T., Herder, Christian, Horikoshi, Momoko, Howard, Annie-Green, Hsueh, Willa, Huang, Mengna, Huang, Wei, Hung, Yi-Jen, Hwang, Mi Yeong, Hwu, Chii-Min, Ichihara, Sahoko, Ikram, Mohammad Arfan, Ingelsson, Martin, Islam, Md. Tariqul, Isono, Masato, Jang, Hye-Mi, Jasmine, Farzana, Jiang, Guozhi, Jonas, Jost B., Jørgensen, Torben, Kamanu, Frederick K., Kandeel, Fouad R., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kaur, Varinderpal, Kawaguchi, Takahisa, Keaton, Jacob M., Kho, Abel N., Khor, Chiea-Chuen, Kibriya, Muhammad G., Kim, Duk-Hwan, Kronenberg, Florian, Kuusisto, Johanna, Läll, Kristi, Lange, Leslie A., Lee, Kyung Min, Lee, Myung-Shik, Lee, Nanette R., Leong, Aaron, Li, Liming, Li, Yun, Li-Gao, Ruifang, Ligthart, Symen, Lindgren, Cecilia M., Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Locke, Adam E., Louie, Tin, Luan, Jian’an, Luk, Andrea O., Luo, Xi, Lv, Jun, Lynch, Julie A., Lyssenko, Valeriya, Maeda, Shiro, Mamakou, Vasiliki, Mansuri, Sohail Rafik, Matsuda, Koichi, Meitinger, Thomas, Melander, Olle, Metspalu, Andres, Mo, Huan, Morris, Andrew D., Moura, Filipe A., Nadler, Jerry L., Nalls, Michael A., Nayak, Uma, Ntalla, Ioanna, Okada, Yukinori, Orozco, Lorena, Patel, Sanjay R., Patil, Snehal, Pei, Pei, Pereira, Mark A., Peters, Annette, Pirie, Fraser J., Polikowsky, Hannah G., Porneala, Bianca, Prasad, Gauri, Rasmussen-Torvik, Laura J., Reiner, Alexander P., Roden, Michael, Rohde, Rebecca, Roll, Katheryn, Sabanayagam, Charumathi, Sandow, Kevin, Sankareswaran, Alagu, Sattar, Naveed, Schönherr, Sebastian, Shahriar, Mohammad, Shen, Botong, Shi, Jinxiu, Shin, Dong Mun, Shojima, Nobuhiro, Smith, Jennifer A., So, Wing Yee, Stančáková, Alena, Steinthorsdottir, Valgerdur, Stilp, Adrienne M., Strauch, Konstantin, Taylor, Kent D., Thorand, Barbara, Thorsteinsdottir, Unnur, Tomlinson, Brian, Tran, Tam C., Tsai, Fuu-Jen, Tuomilehto, Jaakko, Tusie-Luna, Teresa, Udler, Miriam S., Valladares-Salgado, Adan, van Dam, Rob M., van Klinken, Jan B., Varma, Rohit, Wacher-Rodarte, Niels, Wheeler, Eleanor, Wickremasinghe, Ananda R., van Dijk, Ko Willems, Witte, Daniel R., Yajnik, Chittaranjan S., Yamamoto, Ken, Yamamoto, Kenichi, Yoon, Kyungheon, Yu, Canqing, Yuan, Jian-Min, Yusuf, Salim, Zawistowski, Matthew, Zhang, Liang, Zheng, Wei, Raffel, Leslie J., Igase, Michiya, Ipp, Eli, Redline, Susan, Cho, Yoon Shin, Lind, Lars, Province, Michael A., Fornage, Myriam, Hanis, Craig L., Ingelsson, Erik, Zonderman, Alan B., Psaty, Bruce M., Wang, Ya-Xing, Rotimi, Charles N., Becker, Diane M., Matsuda, Fumihiko, Liu, Yongmei, Yokota, Mitsuhiro, Kardia, Sharon L. R., Peyser, Patricia A., Pankow, James S., Engert, James C., Bonnefond, Amélie, Froguel, Philippe, Wilson, James G., Sheu, Wayne H. H., Wu, Jer-Yuarn, Hayes, M. Geoffrey, Ma, Ronald C. W., Wong, Tien-Yin, Mook-Kanamori, Dennis O., Tuomi, Tiinamaija, Chandak, Giriraj R., Collins, Francis S., Bharadwaj, Dwaipayan, Paré, Guillaume, Sale, Michèle M., Ahsan, Habibul, Motala, Ayesha A., Shu, Xiao-Ou, Park, Kyong-Soo, Jukema, J. Wouter, Cruz, Miguel, Chen, Yii-Der Ida, Rich, Stephen S., McKean-Cowdin, Roberta, Grallert, Harald, Cheng, Ching-Yu, Ghanbari, Mohsen, Tai, E-Shyong, Dupuis, Josee, Kato, Norihiro, Laakso, Markku, Köttgen, Anna, Koh, Woon-Puay, Bowden, Donald W., Palmer, Colin N. A., Kooner, Jaspal S., Kooperberg, Charles, Liu, Simin, North, Kari E., Saleheen, Danish, Hansen, Torben, Pedersen, Oluf, Wareham, Nicholas J., Lee, Juyoung, Kim, Bong-Jo, Millwood, Iona Y., Walters, Robin G., Stefansson, Kari, Ahlqvist, Emma, Goodarzi, Mark O., Mohlke, Karen L., Langenberg, Claudia, Haiman, Christopher A., Loos, Ruth J. F., Florez, Jose C., Rader, Daniel J., Ritchie, Marylyn D., Zöllner, Sebastian, Mägi, Reedik, Marston, Nicholas A., Ruff, Christian T., van Heel, David A., Finer, Sarah, Denny, Joshua C., Yamauchi, Toshimasa, Kadowaki, Takashi, Chambers, John C., Ng, Maggie C. Y., Sim, Xueling, Below, Jennifer E., Tsao, Philip S., Chang, Kyong-Mi, McCarthy, Mark I., Meigs, James B., Mahajan, Anubha, Spracklen, Cassandra N., Mercader, Josep M., Boehnke, Michael, Rotter, Jerome I., Vujkovic, Marijana, Voight, Benjamin F., Morris, Andrew P., and Zeggini, Eleftheria

Published
2024
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17. Systemic Markers of Lung Function and Forced Expiratory Volume in 1 Second Decline across Diverse Cohorts.

Author

Ngo, Debby, Pratte, Katherine, Flexeder, Claudia, Petersen, Hans, Dang, Hong, Ma, Yanlin, Keyes, Michelle, Gao, Yan, Deng, Shuliang, Peterson, Bennet, Farrell, Laurie, Bhambhani, Victoria, Palacios, Cesar, Quadir, Juweria, Gillenwater, Lucas, Xu, Hanfei, Emson, Claire, Gieger, Christian, Suhre, Karsten, Graumann, Johannes, Jain, Deepti, Conomos, Matthew, Tracy, Russell, Guo, Xiuqing, Liu, Yongmei, Johnson, W, Cornell, Elaine, Durda, Peter, Taylor, Kent, Papanicolaou, George, Rich, Stephen, Rotter, Jerome, Rennard, Steven, Curtis, Jeffrey, Woodruff, Prescott, Comellas, Alejandro, Silverman, Edwin, Crapo, James, Larson, Martin, Vasan, Ramachandran, Wang, Thomas, Correa, Adolfo, Sims, Mario, Wilson, James, Gerszten, Robert, OConnor, George, Barr, R, Couper, David, Dupuis, Josée, Manichaikul, Ani, ONeal, Wanda, Tesfaigzi, Yohannes, Schulz, Holger, and Bowler, Russell

Subjects
airflow obstruction, biomarkers, proteomics, Humans, Forced Expiratory Volume, Lung, Proteomics, Pulmonary Disease, Chronic Obstructive, Vital Capacity, Spirometry, Biomarkers
Abstract

Rationale: Chronic obstructive pulmonary disease (COPD) is a complex disease characterized by airway obstruction and accelerated lung function decline. Our understanding of systemic protein biomarkers associated with COPD remains incomplete. Objectives: To determine what proteins and pathways are associated with impaired pulmonary function in a diverse population. Methods: We studied 6,722 participants across six cohort studies with both aptamer-based proteomic and spirometry data (4,566 predominantly White participants in a discovery analysis and 2,156 African American cohort participants in a validation). In linear regression models, we examined protein associations with baseline forced expiratory volume in 1 second (FEV1) and FEV1/forced vital capacity (FVC). In linear mixed effects models, we investigated the associations of baseline protein levels with rate of FEV1 decline (ml/yr) in 2,777 participants with up to 7 years of follow-up spirometry. Results: We identified 254 proteins associated with FEV1 in our discovery analyses, with 80 proteins validated in the Jackson Heart Study. Novel validated protein associations include kallistatin serine protease inhibitor, growth differentiation factor 2, and tumor necrosis factor-like weak inducer of apoptosis (discovery β = 0.0561, Q = 4.05 × 10-10; β  = 0.0421, Q = 1.12 × 10-3; and β = 0.0358, Q = 1.67 × 10-3, respectively). In longitudinal analyses within cohorts with follow-up spirometry, we identified 15 proteins associated with FEV1 decline (Q

Published
2023

18. Multiset correlation and factor analysis enables exploration of multi-omics data

Author

Brown, Brielin C, Wang, Collin, Kasela, Silva, Aguet, François, Nachun, Daniel C, Taylor, Kent D, Tracy, Russell P, Durda, Peter, Liu, Yongmei, Johnson, W Craig, Van Den Berg, David, Gupta, Namrata, Gabriel, Stacy, Smith, Joshua D, Gerzsten, Robert, Clish, Clary, Wong, Quenna, Papanicolau, George, Blackwell, Thomas W, Rotter, Jerome I, Rich, Stephen S, Barr, R Graham, Ardlie, Kristin G, Knowles, David A, and Lappalainen, Tuuli

Subjects
Biological Sciences, Genetics, Aging, Biotechnology, Cardiovascular, Human Genome, 2.5 Research design and methodologies (aetiology), Aetiology, Good Health and Well Being
Abstract

Multi-omics datasets are becoming more common, necessitating better integration methods to realize their revolutionary potential. Here, we introduce multi-set correlation and factor analysis (MCFA), an unsupervised integration method tailored to the unique challenges of high-dimensional genomics data that enables fast inference of shared and private factors. We used MCFA to integrate methylation markers, protein expression, RNA expression, and metabolite levels in 614 diverse samples from the Trans-Omics for Precision Medicine/Multi-Ethnic Study of Atherosclerosis multi-omics pilot. Samples cluster strongly by ancestry in the shared space, even in the absence of genetic information, while private spaces frequently capture dataset-specific technical variation. Finally, we integrated genetic data by conducting a genome-wide association study (GWAS) of our inferred factors, observing that several factors are enriched for GWAS hits and trans-expression quantitative trait loci. Two of these factors appear to be related to metabolic disease. Our study provides a foundation and framework for further integrative analysis of ever larger multi-modal genomic datasets.

Published
2023

19. TRAC: A Textual Benchmark for Reasoning about Actions and Change

Author

He, Weinan, Huang, Canming, Xiao, Zhanhao, and Liu, Yongmei

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Reasoning about actions and change (RAC) is essential to understand and interact with the ever-changing environment. Previous AI research has shown the importance of fundamental and indispensable knowledge of actions, i.e., preconditions and effects. However, traditional methods rely on logical formalization which hinders practical applications. With recent transformer-based language models (LMs), reasoning over text is desirable and seemingly feasible, leading to the question of whether LMs can effectively and efficiently learn to solve RAC problems. We propose four essential RAC tasks as a comprehensive textual benchmark and generate problems in a way that minimizes the influence of other linguistic requirements (e.g., grounding) to focus on RAC. The resulting benchmark, TRAC, encompassing problems of various complexities, facilitates a more granular evaluation of LMs, precisely targeting the structural generalization ability much needed for RAC. Experiments with three high-performing transformers indicates that additional efforts are needed to tackle challenges raised by TRAC.

Published
2022

22. Gene expression associations with body mass index in the Multi-Ethnic Study of Atherosclerosis

Author

Vargas, Luciana B, Lange, Leslie A, Ferrier, Kendra, Aguet, François, Ardlie, Kristin, Gabriel, Stacey, Gupta, Namrata, Smith, Joshua D, Blackwell, Thomas W, Ding, Jingzhong, Durda, Peter, Tracy, Russell P, Liu, Yongmei, Taylor, Kent D, Craig Johnson, W, Rich, Stephen S, Rotter, Jerome I, Lange, Ethan M, and Konigsberg, Iain R

Subjects
Epidemiology, Health Sciences, Obesity, Minority Health, Atherosclerosis, Genetics, Nutrition, Health Disparities, Prevention, Human Genome, 2.1 Biological and endogenous factors, Metabolic and endocrine, Oral and gastrointestinal, Stroke, Cardiovascular, Adult, Child, Humans, Body Mass Index, Gene Expression, Medical and Health Sciences, Education, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

Background/objectivesObesity, defined as excessive fat accumulation that represents a health risk, is increasing in adults and children, reaching global epidemic proportions. Body mass index (BMI) correlates with body fat and future health risk, yet differs in prediction by fat distribution, across populations and by age. Nonetheless, few genetic studies of BMI have been conducted in ancestrally diverse populations. Gene expression association with BMI was assessed in the Multi-Ethnic Study of Atherosclerosis (MESA) in four self-identified race and ethnicity (SIRE) groups to identify genes associated with obesity.Subjects/methodsRNA-sequencing was performed on 1096 MESA participants (37.8% white, 24.3% Hispanic, 28.4% African American, and 9.5% Chinese American) and linear models were used to assess the association of expression from each gene for its effect on BMI, adjusting for age, sex, sequencing center, study site, five expression and four genetic principal components in each self-identified race group. Sample-size-weighted meta-analysis was performed to identify genes with BMI-associated expression across ancestry groups.ResultsWithin individual SIRE groups, there were zero to three genes whose expression is significantly (p

Published
2023

23. The association between aging-related monocyte transcriptional networks and comorbidity burden: the Multi-Ethnic Study of Atherosclerosis (MESA)

Author

Ding, Jingzhong, Lohman, Kurt, Molina, Anthony, Delbono, Osvaldo, Bertoni, Alain, Shea, Steven, Post, Wendy, Guo, Xiuqing, Barr, R Graham, Manichaikul, Ani W, Pankow, James S, Rotter, Jerome I, Hoeschele, Ina, Kritchevsky, Stephen B, and Liu, Yongmei

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Clinical Sciences, Genetics, Cardiovascular, Clinical Research, Aging, Atherosclerosis, Minority Health, Women's Health, Health Disparities, 2.1 Biological and endogenous factors, Good Health and Well Being, Humans, Female, Male, Monocytes, Gene Regulatory Networks, Receptors, IgG, Comorbidity, Monocyte, Transcriptomic, Clinical sciences
Abstract

Translating our knowledge of the biological aging from animal models to humans may give rise to novel approaches of targeting multiple aging-related diseases simultaneously and increasing health span. Here, for the first time, we use transcriptomic signatures of monocytes to identify biological aging pathways underlying multiple aging-related diseases in humans. The ordinal logistic regression was used to cross-sectionally investigate transcriptomics of the comorbidity index in 1264 community-based Multi-Ethnic Study of Atherosclerosis (MESA) adults, 47% Caucasian, 32% Hispanic, 21% African American, and 51% female, aged 55-94 years. The comorbidity index was defined as the number of prevalent aging-related diseases including cardiovascular disease, type-2 diabetes, hypertension, cancer, dementia, chronic kidney disease, chronic obstructive pulmonary disease, and hip fracture. We identified 708 gene transcripts associated with the comorbidity index (FDR

Published
2023

24. Multi-ancestry epigenome-wide analyses identify methylated sites associated with aortic augmentation index in TOPMed MESA

Author

Hu, Xiaowei, Logan, Jeongok G, Kwon, Younghoon, Lima, Joao AC, Jacobs, David R, Duprez, Daniel, Brumback, Lyndia, Taylor, Kent D, Durda, Peter, Johnson, W Craig, Cornell, Elaine, Guo, Xiuqing, Liu, Yongmei, Tracy, Russell P, Blackwell, Thomas W, Papanicolaou, George, Mitchell, Gary F, Rich, Stephen S, Rotter, Jerome I, Van Den Berg, David J, Chirinos, Julio A, Hughes, Timothy M, Garrett-Bakelman, Francine E, and Manichaikul, Ani

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Minority Health, Precision Medicine, Health Disparities, Clinical Research, Cardiovascular, Human Genome, Atherosclerosis, 2.1 Biological and endogenous factors, Good Health and Well Being, Humans, Epigenesis, Genetic, Epigenome, Transforming Growth Factor beta3, Genome-Wide Association Study, DNA Methylation, CpG Islands
Abstract

Despite the prognostic value of arterial stiffness (AS) and pulsatile hemodynamics (PH) for cardiovascular morbidity and mortality, epigenetic modifications that contribute to AS/PH remain unknown. To gain a better understanding of the link between epigenetics (DNA methylation) and AS/PH, we examined the relationship of eight measures of AS/PH with CpG sites and co-methylated regions using multi-ancestry participants from Trans-Omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA) with sample sizes ranging from 438 to 874. Epigenome-wide association analysis identified one genome-wide significant CpG (cg20711926-CYP1B1) associated with aortic augmentation index (AIx). Follow-up analyses, including gene set enrichment analysis, expression quantitative trait methylation analysis, and functional enrichment analysis on differentially methylated positions and regions, further prioritized three CpGs and their annotated genes (cg23800023-ETS1, cg08426368-TGFB3, and cg17350632-HLA-DPB1) for AIx. Among these, ETS1 and TGFB3 have been previously prioritized as candidate genes. Furthermore, both ETS1 and HLA-DPB1 have significant tissue correlations between Whole Blood and Aorta in GTEx, which suggests ETS1 and HLA-DPB1 could be potential biomarkers in understanding pathophysiology of AS/PH. Overall, our findings support the possible role of epigenetic regulation via DNA methylation of specific genes associated with AIx as well as identifying potential targets for regulation of AS/PH.

Published
2023

25. Canonical correlation analysis for multi-omics: Application to cross-cohort analysis

Author

Jiang, Min-Zhi, Aguet, François, Ardlie, Kristin, Chen, Jiawen, Cornell, Elaine, Cruz, Dan, Durda, Peter, Gabriel, Stacey B, Gerszten, Robert E, Guo, Xiuqing, Johnson, Craig W, Kasela, Silva, Lange, Leslie A, Lappalainen, Tuuli, Liu, Yongmei, Reiner, Alex P, Smith, Josh, Sofer, Tamar, Taylor, Kent D, Tracy, Russell P, VanDenBerg, David J, Wilson, James G, Rich, Stephen S, Rotter, Jerome I, Love, Michael I, Raffield, Laura M, Li, Yun, and Consortium, TOPMed Analysis Working Group NHLBI Trans-Omics for Precision Medicine

Subjects
Biological Sciences, Genetics, Atherosclerosis, Cardiovascular, Generic health relevance, Humans, Proteomics, Canonical Correlation Analysis, Multiomics, Cohort Studies, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Analysis Working Group, Developmental Biology
Abstract

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

Published
2023

26. Distinct COPD subtypes in former smokers revealed by gene network perturbation analysis

Author

Buschur, Kristina L, Riley, Craig, Saferali, Aabida, Castaldi, Peter, Zhang, Grace, Aguet, Francois, Ardlie, Kristin G, Durda, Peter, Craig Johnson, W, Kasela, Silva, Liu, Yongmei, Manichaikul, Ani, Rich, Stephen S, Rotter, Jerome I, Smith, Josh, Taylor, Kent D, Tracy, Russell P, Lappalainen, Tuuli, Graham Barr, R, Sciurba, Frank, Hersh, Craig P, and Benos, Panayiotis V

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Clinical Research, Chronic Obstructive Pulmonary Disease, Genetics, Lung, 2.1 Biological and endogenous factors, Aetiology, Respiratory, Good Health and Well Being, Humans, Gene Regulatory Networks, Smokers, Genome-Wide Association Study, Pulmonary Disease, Chronic Obstructive, Prognosis, COPD, Graphical models, Gene expression, Disease subtypes, Cardiorespiratory Medicine and Haematology, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundChronic obstructive pulmonary disease (COPD) varies significantly in symptomatic and physiologic presentation. Identifying disease subtypes from molecular data, collected from easily accessible blood samples, can help stratify patients and guide disease management and treatment.MethodsBlood gene expression measured by RNA-sequencing in the COPDGene Study was analyzed using a network perturbation analysis method. Each COPD sample was compared against a learned reference gene network to determine the part that is deregulated. Gene deregulation values were used to cluster the disease samples.ResultsThe discovery set included 617 former smokers from COPDGene. Four distinct gene network subtypes are identified with significant differences in symptoms, exercise capacity and mortality. These clusters do not necessarily correspond with the levels of lung function impairment and are independently validated in two external cohorts: 769 former smokers from COPDGene and 431 former smokers in the Multi-Ethnic Study of Atherosclerosis (MESA). Additionally, we identify several genes that are significantly deregulated across these subtypes, including DSP and GSTM1, which have been previously associated with COPD through genome-wide association study (GWAS).ConclusionsThe identified subtypes differ in mortality and in their clinical and functional characteristics, underlining the need for multi-dimensional assessment potentially supplemented by selected markers of gene expression. The subtypes were consistent across cohorts and could be used for new patient stratification and disease prognosis.

Published
2023

27. Corrigendum

Author

Ding, Jingzhong, Nguyen, Anh Tram, Lohman, Kurt, Hensley, Michael T., Parker, Daniel, Hou, Li, Taylor, Jackson, Voora, Deepak, Sawyer, Janet K., Boudyguina, Elena, Bancks, Michael P., Bertoni, Alain, Pankow, James S., Rotter, Jerome I., Goodarzi, Mark O., Tracy, Russell P., Murdoch, David M., Duprez, Daniel, Rich, Stephen S., Psaty, Bruce M., Siscovick, David, Newgard, Christopher B., Herrington, David, Hoeschele, Ina, Shea, Steven, Stein, James H., Patel, Manesh, Post, Wendy, Jacobs, David, Jr., Parks, John S., and Liu, Yongmei

Abstract

Original citation: J Clin Invest. 2024; 134(10):e173278. https://doi.org/10.1172/JCI173278. Citation for this corrigendum: J Clin Invest. 2024;134(16):e185290. https://doi.org/10.1172/JCI185290 During the preparation of this manuscript, Daniel Duprez's information was inadvertently omitted from [...]

Published
2024
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28. Automatic Verification of Sound Abstractions for Generalized Planning

Author

Cui, Zhenhe, Kuang, Weidu, and Liu, Yongmei

Subjects
Computer Science - Artificial Intelligence
Abstract

Generalized planning studies the computation of general solutions for a set of planning problems. Computing general solutions with correctness guarantee has long been a key issue in generalized planning. Abstractions are widely used to solve generalized planning problems. Solutions of sound abstractions are those with correctness guarantees for generalized planning problems. Recently, Cui et al. proposed a uniform abstraction framework for generalized planning. They gave the model-theoretic definitions of sound and complete abstractions for generalized planning problems. In this paper, based on Cui et al.'s work, we explore automatic verification of sound abstractions for generalized planning. We firstly present the proof-theoretic characterization for sound abstraction. Then, based on the characterization, we give a sufficient condition for sound abstractions which is first-order verifiable. To implement it, we exploit regression extensions, and develop methods to handle counting and transitive closure. Finally, we implement a sound abstraction verification system and report experimental results on several domains., Comment: 11 pages

Published
2022

29. Methods for Large-scale Single Mediator Hypothesis Testing: Possible Choices and Comparisons

Author

Du, Jiacong, Zhou, Xiang, Hao, Wei, Liu, Yongmei, Smith, Jennifer A., and Mukherjee, Bhramar

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Mediation hypothesis testing for a large number of mediators is challenging due to the composite structure of the null hypothesis, H0:alpha*beta=0 (alpha: effect of the exposure on the mediator after adjusting for confounders; beta: effect of the mediator on the outcome after adjusting for exposure and confounders). In this paper, we reviewed three classes of methods for multiple mediation hypothesis testing. In addition to these existing methods, we developed the Sobel-comp method, which uses a corrected mixture reference distribution for Sobel's test statistic. We performed extensive simulation studies to compare all six methods in terms of the false positive rates under the null hypothesis and the true positive rates under the alternative hypothesis. We found that the class of methods which uses a mixture reference distribution could best maintain the false positive rates at the nominal level under the null hypothesis and had the greatest true positive rates under the alternative hypothesis. We applied all methods to study the mediation mechanism of DNA methylation sites in the pathway from adult socioeconomic status to glycated hemoglobin level using data from the Multi-Ethnic Study of Atherosclerosis (MESA). We also provide guidelines for choosing the optimal mediation hypothesis testing method in practice., Comment: 24 pages, 6 figures, 4 tables

Published
2022

30. Targeted Genome Sequencing Identifies Multiple Rare Variants in Caveolin-1 Associated with Obstructive Sleep Apnea.

Author

Liang, Jingjing, Wang, Heming, Cade, Brian E, Kurniansyah, Nuzulul, He, Karen Y, Lee, Jiwon, Sands, Scott A, A Brody, Jennifer, Chen, Han, Gottlieb, Daniel J, Evans, Daniel S, Guo, Xiuqing, Gharib, Sina A, Hale, Lauren, Hillman, David R, Lutsey, Pamela L, Mukherjee, Sutapa, Ochs-Balcom, Heather M, Palmer, Lyle J, Purcell, Shaun, Saxena, Richa, Patel, Sanjay R, Stone, Katie L, Tranah, Gregory J, Boerwinkle, Eric, Lin, Xihong, Liu, Yongmei, Psaty, Bruce M, Vasan, Ramachandran S, Manichaikul, Ani, Rich, Stephen S, Rotter, Jerome I, Sofer, Tamar, Redline, Susan, and Zhu, Xiaofeng

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Sleep Research, Biotechnology, Cardiovascular, Human Genome, Genetics, Minority Health, Precision Medicine, Lung, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Humans, Caveolin 1, Sleep Apnea, Obstructive, Sequence Analysis, DNA, High-Throughput Nucleotide Sequencing, obstructive sleep apnea, caveolin-1, apnea-hypopnea index, genetic association analysis, rare variants, TOPMed Sleep Working Group, apnea–hypopnea index, Medical and Health Sciences, Respiratory System, Cardiovascular medicine and haematology, Clinical sciences
Abstract

Rationale: Obstructive sleep apnea (OSA) is a common disorder associated with increased risk for cardiovascular disease, diabetes, and premature mortality. There is strong clinical and epidemiologic evidence supporting the importance of genetic factors influencing OSA but limited data implicating specific genes. Objectives: To search for rare variants contributing to OSA severity. Methods: Leveraging high-depth genomic sequencing data from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and imputed genotype data from multiple population-based studies, we performed linkage analysis in the CFS (Cleveland Family Study), followed by multistage gene-based association analyses in independent cohorts for apnea-hypopnea index (AHI) in a total of 7,708 individuals of European ancestry. Measurements and Main Results: Linkage analysis in the CFS identified a suggestive linkage peak on chromosome 7q31 (LOD = 2.31). Gene-based analysis identified 21 noncoding rare variants in CAV1 (Caveolin-1) associated with lower AHI after accounting for multiple comparisons (P = 7.4 × 10-8). These noncoding variants together significantly contributed to the linkage evidence (P

Published
2022

31. Differences in Metabolomic Profiles Between Black and White Women and Risk of Coronary Heart Disease: an Observational Study of Women From Four US Cohorts

Author

Hu, Jie, Yao, Jie, Deng, Shuliang, Balasubramanian, Raji, Jiménez, Monik C, Li, Jun, Guo, Xiuqing, Cruz, Daniel E, Gao, Yan, Huang, Tianyi, Zeleznik, Oana A, Ngo, Debby, Liu, Simin, Rosal, Milagros C, Nassir, Rami, Paynter, Nina P, Albert, Christine M, Tracy, Russell P, Durda, Peter, Liu, Yongmei, Taylor, Kent D, Johnson, W Craig, Sun, Qi, Rimm, Eric B, Eliassen, A Heather, Rich, Stephen S, Rotter, Jerome I, Gerszten, Robert E, Clish, Clary B, and Rexrode, Kathryn M

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Heart Disease - Coronary Heart Disease, Atherosclerosis, Aging, Cardiovascular, Prevention, Clinical Research, Heart Disease, Good Health and Well Being, Amino Acids, Coronary Disease, Female, Hormones, Humans, Lipids, Risk Factors, United States, heart diseases, health status disparities, metabolomics, plasma, race, women, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundRacial differences in metabolomic profiles may reflect underlying differences in social determinants of health by self-reported race and may be related to racial disparities in coronary heart disease (CHD) among women in the United States. However, the magnitude of differences in metabolomic profiles between Black and White women in the United States has not been well-described. It also remains unknown whether such differences are related to differences in CHD risk.MethodsPlasma metabolomic profiles were analyzed using liquid chromatography-tandem mass spectrometry in the WHI-OS (Women's Health Initiative-Observational Study; 138 Black and 696 White women), WHI-HT trials (WHI-Hormone Therapy; 156 Black and 1138 White women), MESA (Multi-Ethnic Study of Atherosclerosis; 114 Black and 219 White women), JHS (Jackson Heart Study; 1465 Black women with 107 incident CHD cases), and NHS (Nurses' Health Study; 2506 White women with 136 incident CHD cases). First, linear regression models were used to estimate associations between self-reported race and 472 metabolites in WHI-OS (discovery); findings were replicated in WHI-HT and validated in MESA. Second, we used elastic net regression to construct a racial difference metabolomic pattern (RDMP) representing differences in the metabolomic patterns between Black and White women in the WHI-OS; the RDMP was validated in the WHI-HT and MESA. Third, using conditional logistic regressions in the WHI (717 CHD cases and 719 matched controls), we examined associations of metabolites with large differences in levels by race and the RDMP with risk of CHD, and the results were replicated in Black women from the JHS and White women from the NHS.ResultsOf the 472 tested metabolites, levels of 259 (54.9%) metabolites, mostly lipid metabolites and amino acids, significantly differed between Black and White women in both WHI-OS and WHI-HT after adjusting for baseline characteristics, socioeconomic status, lifestyle factors, baseline health conditions, and medication use (false discovery rate

Published
2022

33. Preclinical study and phase II trial of adapting low-dose radiotherapy to immunotherapy in small cell lung cancer

Author

Wang, Hui, Yao, Zhuoran, Kang, Kai, Zhou, Lin, Xiu, Weigang, Sun, Jianguo, Xie, Conghua, Yu, Min, Li, Yanying, Zhang, Yan, Zheng, Yue, Lin, Guo, Pan, Xiangyu, Wu, Yijun, Luo, Ren, Wang, Laduona, Tang, Min, Liao, Shuangsi, Zhu, Jiang, Zhou, Xiaojuan, Zhang, Xuanwei, Xu, Yong, Liu, Yongmei, Peng, Feng, Wang, Jin, Xiang, Lisha, Yin, Limei, Deng, Lei, Huang, Meijuan, Gong, Youling, Zou, Bingwen, Wu, Lin, Yuan, Zhiyong, Bi, Nan, Fan, Min, Xu, Yaping, Tong, Ruizhan, Yi, Linglu, Gan, Lu, Xue, Jianxin, Mo, Xianming, Chen, Chong, Na, Feifei, and Lu, You

Published
2024
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34. Arsenic Exposure, Blood DNA Methylation, and Cardiovascular Disease

Author

Domingo-Relloso, Arce, Makhani, Kiran, Riffo-Campos, Angela L, Tellez-Plaza, Maria, Klein, Kathleen Oros, Subedi, Pooja, Zhao, Jinying, Moon, Katherine A, Bozack, Anne K, Haack, Karin, Goessler, Walter, Umans, Jason G, Best, Lyle G, Zhang, Ying, Herreros-Martinez, Miguel, Glabonjat, Ronald A, Schilling, Kathrin, Galvez-Fernandez, Marta, Kent, Jack W, Sanchez, Tiffany R, Taylor, Kent D, Johnson, W Craig, Durda, Peter, Tracy, Russell P, Rotter, Jerome I, Rich, Stephen S, Van Den Berg, David, Kasela, Silva, Lappalainen, Tuuli, Vasan, Ramachandran S, Joehanes, Roby, Howard, Barbara V, Levy, Daniel, Lohman, Kurt, Liu, Yongmei, Fallin, M Daniele, Cole, Shelley A, Mann, Koren K, and Navas-Acien, Ana

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Aging, Genetics, American Indian or Alaska Native, Prevention, Cardiovascular, Heart Disease, Atherosclerosis, Good Health and Well Being, Animals, Apolipoproteins E, Arsenic, Cardiovascular Diseases, DNA Methylation, Female, Humans, Male, Mice, Middle Aged, Prospective Studies, arsenic, cardiovascular diseases, DNA methylation, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

BackgroundEpigenetic dysregulation has been proposed as a key mechanism for arsenic-related cardiovascular disease (CVD). We evaluated differentially methylated positions (DMPs) as potential mediators on the association between arsenic and CVD.MethodsBlood DNA methylation was measured in 2321 participants (mean age 56.2, 58.6% women) of the Strong Heart Study, a prospective cohort of American Indians. Urinary arsenic species were measured using high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry. We identified DMPs that are potential mediators between arsenic and CVD. In a cross-species analysis, we compared those DMPs with differential liver DNA methylation following early-life arsenic exposure in the apoE knockout (apoE-/-) mouse model of atherosclerosis.ResultsA total of 20 and 13 DMPs were potential mediators for CVD incidence and mortality, respectively, several of them annotated to genes related to diabetes. Eleven of these DMPs were similarly associated with incident CVD in 3 diverse prospective cohorts (Framingham Heart Study, Women's Health Initiative, and Multi-Ethnic Study of Atherosclerosis). In the mouse model, differentially methylated regions in 20 of those genes and DMPs in 10 genes were associated with arsenic.ConclusionsDifferential DNA methylation might be part of the biological link between arsenic and CVD. The gene functions suggest that diabetes might represent a relevant mechanism for arsenic-related cardiovascular risk in populations with a high burden of diabetes.

Published
2022

35. Genetic interactions drive heterogeneity in causal variant effect sizes for gene expression and complex traits

Author

Patel, Roshni A, Musharoff, Shaila A, Spence, Jeffrey P, Pimentel, Harold, Tcheandjieu, Catherine, Mostafavi, Hakhamanesh, Sinnott-Armstrong, Nasa, Clarke, Shoa L, Smith, Courtney J, Program, VA Million Veteran, Durda, Peter P, Taylor, Kent D, Tracy, Russell, Liu, Yongmei, Johnson, W Craig, Aguet, Francois, Ardlie, Kristin G, Gabriel, Stacey, Smith, Josh, Nickerson, Deborah A, Rich, Stephen S, Rotter, Jerome I, Tsao, Philip S, Assimes, Themistocles L, and Pritchard, Jonathan K

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Atherosclerosis, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Cholesterol, LDL, Gene Expression, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Polymorphism, Single Nucleotide, White People, V.A. Million Veteran Program, complex traits, genetic correlation, genome-wide association, population genetics, statistical genetics, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Despite the growing number of genome-wide association studies (GWASs), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWASs are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate, although this is not significant, most likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

Published
2022

36. One-size-fits-all versus risk-category-based screening interval strategies for cardiovascular disease prevention in Chinese adults: a prospective cohort study

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Li, Liming, Lv, Jun, Peto, Richard, Walters, Robin, Avery, Daniel, Barnard, Maxim, Bennett, Derrick, Belbasis, Lazaros, Boxall, Ruth, Chan, Ka Hung, Chen, Yiping, Clarke, Charlotte, Clarke, Johnathan, Du, Huaidong, Mohamed, Ahmed Edris, Fry, Hannah, Gilbert, Simon, Im, Pek Kei, Iona, Andri, Kakkoura, Maria, Kartsonaki, Christiana, Lam, Hubert, Lin, Kuang, Liu, James, Mazidi, Mohsen, Millwood, Iona, Morris, Sam, Nie, Qunhua, Pozarickij, Alfred, Rahmati, Maryanm, Ryder, Paul, Said, Saredo, Schmidt, Dan, Stevens, Becky, Turnbull, Iain, Wang, Baihan, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Yao, Pang, Han, Xiao, Hou, Can, Xia, Qingmei, Liu, Chao, Pei, Pei, Sun, Dianjianyi, Yu, Canqing, Pan, Lang, Pang, Zengchang, Gao, Ruqin, Li, Shanpeng, Duan, Haiping, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Zhai, Yaoming, Ning, Feng, Sun, Xiaohui, Li, Feifei, Lv, Silu, Wang, Junzheng, Hou, Wei, Sun, Wei, Yan, Shichun, Cui, Xiaoming, Wang, Chi, Wu, Zhenyuan, Li, Yanjie, Kang, Quan, Luo, Huiming, Ou, Tingting, Zheng, Xiangyang, Guo, Zhendong, Wu, Shukuan, Li, Yilei, Li, Huimei, Wu, Ming, Zhou, Yonglin, Zhou, Jinyi, Tao, Ran, Yang, Jie, Fang Liu, Jian Su., Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Lin, Mei, Lu, Zhenzhen, Zhou, Lifang, Xie, Changping, Lan, Jian, Zhu, Tingping, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Chen, Ningyu, Qin, Yulu, Wang, Sisi, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Chang, Xiaoyu, Yuan, Mingqiang, Wu, Xia, Jiang, Wei, Liu, Jiaqiu, Sun, Qiang, Chen, Faqing, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, Zhang, Xi, Kang, Kai, Feng, Shixian, Tian, Huizi, Fan, Lei, Li, XiaoLin, Sun, Huarong, He, Pan, Zhang, Xukui, Yu, Min, Hu, Ruying, Wang, Hao, Zhang, Xiaoyi, Cao, Yuan, Xie, Kaixu, Chen, Lingli, Shen, Dun, Li, Xiaojun, Jin, Donghui, Yin, Li, Liu, Huilin, Fu, Zhongxi, Xu, Xin, Zhang, Hao, Chen, Jianwei, Peng, Yuan, Zhang, Libo, Qu, Chan, Sun, Zhijia, Ma, Yu, and Pang, Yuanjie

Published
2024
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39. Prioritization of Kidney Cell Types Highlights Myofibroblast Cells in Regulating Human Blood Pressure

Author

Evangelou, Evangelos, Warren, Helen R., Gao, He, Ntritsos, Georgios, Dimou, Niki, Esko, Tonu, Mägi, Reedik, Milani, Lili, Almgren, Peter, Boutin, Thibaud, Debette, Stéphanie, Ding, Jun, Giulianini, Franco, Holliday, Elizabeth G., Jackson, Anne U., Li -Gao, Ruifang, Lin, Wei -Yu, Luan, Jian'an, Mangino, Massimo, Oldmeadow, Christopher, Prins, Bram Peter, Qian, Yong, Sargurupremraj, Muralidharan, Shah, Nabi, Surendran, Praveen, Thériault, Sébastien, Verweij, Niek, Willems, Sara M., Zhao, Jing -Hua, Amouyel, Philippe, Connell, John, de Mutsert, Renée, Doney, Alex S.F., Farrall, Martin, Menni, Cristina, Morris, Andrew D., Noordam, Raymond, Paré, Guillaume, Poulter, Neil R., Shields, Denis C., Stanton, Alice, Thom, Simon, Abecasis, Gonçalo, Amin, Najaf, Arking, Dan E., Ayers, Kristin L., Barbieri, Caterina M., Batini, Chiara, Bis, Joshua C., Blake, Tineka, Bochud, Murielle, Boehnke, Michael, Boerwinkle, Eric, Boomsma, Dorret I., Bottinger, Erwin P., Braund, Peter S., Brumat, Marco, Campbell, Archie, Campbell, Harry, Chakravarti, Aravinda, Chambers, John C., Chauhan, Ganesh, Ciullo, Marina, Cocca, Massimiliano, Collins, Francis, Cordell, Heather J., Davies, Gail, de Borst, Martin H., de Geus, Eco J., Deary, Ian J., Deelen, Joris, Del Greco M, Fabiola, Demirkale, Cumhur Yusuf, Dörr, Marcus, Ehret, Georg B., Elosua, Roberto, Enroth, Stefan, Erzurumluoglu, A. Mesut, Ferreira, Teresa, Frånberg, Mattias, Franco, Oscar H., Gandin, Ilaria, Gasparini, Paolo, Giedraitis, Vilmantas, Gieger, Christian, Girotto, Giorgia, Goel, Anuj, Gow, Alan J., Gudnason, Vilmundur, Guo, Xiuqing, Gyllensten, Ulf, Hamsten, Anders, Harris, Tamara B., Harris, Sarah E., Hartman, Catharina A., Havulinna, Aki S., Hicks, Andrew A., Hofer, Edith, Hofman, Albert, Hottenga, Jouke-Jan, Huffman, Jennifer E., Hwang, Shih-Jen, Ingelsson, Erik, James, Alan, Jansen, Rick, Jarvelin, Marjo -Riitta, Joehanes, Roby, Johansson, Åsa, Johnson, Andrew D., Joshi, Peter K., Jousilahti, Pekka, Jukema, J. Wouter, Jula, Antti, Kähönen, Mika, Kathiresan, Sekar, Keavney, Bernard D., Khaw, Kay-Tee, Knekt, Paul, Knight, Joanne, Kolcic, Ivana, Kooner, Jaspal S., Koskinen, Seppo, Kristiansson, Kati, Kutalik, Zoltan, Laan, Maris, Larson, Marty, Launer, Lenore J., Lehne, Benjamin, Lehtimäki, Terho, Liewald, David C.M., Lin, Li, Lind, Lars, Lindgren, Cecilia M., Liu, YongMei, Loos, Ruth J.F., Lopez, Lorna M., Lu, Yingchang, Lyytikäinen, Leo-Pekka, Mahajan, Anubha, Mamasoula, Chrysovalanto, Marrugat, Jaume, Marten, Jonathan, Milaneschi, Yuri, Morgan, Anna, Morris, Andrew P., Morrison, Alanna C., Munson, Peter J., Nalls, Mike A., Nandakumar, Priyanka, Nelson, Christopher P., Niiranen, Teemu, Nolte, Ilja M., Nutile, Teresa, Oldehinkel, Albertine J., Oostra, Ben A., O'Reilly, Paul F., Org, Elin, Padmanabhan, Sandosh, Palmas, Walter, Palotie, Aarno, Pattie, Alison, Penninx, Brenda W.J.H., Perola, Markus, Peters, Annette, Polasek, Ozren, Pramstaller, Peter P., Nguyen, Quang Tri, Raitakari, Olli T., Rettig, Rainer, Rice, Kenneth, Ridker, Paul M., Ried, Janina S., Riese, Harriëtte, Ripatti, Samuli, Robino, Antonietta, Rose, Lynda M., Rotter, Jerome I., Rudan, Igor, Ruggiero, Daniela, Saba, Yasaman, Sala, Cinzia F., Salomaa, Veikko, Samani, Nilesh J., Sarin, Antti-Pekka, Schmidt, Reinhold, Schmidt, Helena, Shrine, Nick, Siscovick, David, Smith, Albert V., Snieder, Harold, Sõber, Siim, Sorice, Rossella, Starr, John M., Stott, David J., Strachan, David P., Strawbridge, Rona J., Sundström, Johan, Swertz, Morris A., Taylor, Kent D., Teumer, Alexander, Tobin, Martin D., Tomaszewski, Maciej, Toniolo, Daniela, Traglia, Michela, Trompet, Stella, Tuomilehto, Jaakko, Tzourio, Christophe, Uitterlinden, André G., Vaez, Ahmad, van der Most, Peter J., van Duijn, Cornelia M., Verwoert, Germaine C., Vitart, Veronique, Völker, Uwe, Vollenweider, Peter, Vuckovic, Dragana, Watkins, Hugh, Wild, Sarah H., Willemsen, Gonneke, Wilson, James F., Wright, Alan F., Yao, Jie, Zemunik, Tatijana, Zhang, Weihua, Attia, John R., Butterworth, Adam S., Chasman, Daniel I., Conen, David, Cucca, Francesco, Danesh, John, Hayward, Caroline, Howson, Joanna M.M., Laakso, Markku, Lakatta, Edward G., Langenberg, Claudia, Melander, Olle, Mook-Kanamori, Dennis O., Palmer, Colin N.A., Risch, Lorenz, Scott, Robert A., Scott, Rodney J., Sever, Peter, Spector, Tim D., van der Harst, Pim, Wareham, Nicholas J., Zeggini, Eleftheria, Levy, Daniel, Munroe, Patricia B., Newton-Cheh, Christopher, Brown, Morris J., Metspalu, Andres, Psaty, Bruce M., Wain, Louise V., Elliott, Paul, Caulfield, Mark J., Ganji-Arjenaki, Mahboube, Kamali, Zoha, Sardari, Soroush, and de Borst, Martin

Published
2024
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42. Epigenome-wide analysis of long-term air pollution exposure and DNA methylation in monocytes: results from the Multi-Ethnic Study of Atherosclerosis

Author

C., Gloria, Liu, Yongmei, MacDonald, James W, Reynolds, Lindsay M, Enquobahrie, Daniel A, Fitzpatrick, Annette L, Kerr, Kathleen F, Budoff, Matthew J, Lee, Su-In, Siscovick, David, and Kaufman, Joel D

Subjects
Biological Sciences, Genetics, Cardiovascular, Climate-Related Exposures and Conditions, Human Genome, Atherosclerosis, Adult, Air Pollutants, Air Pollution, Antigens, Neoplasm, DNA Methylation, Environmental Exposure, Epigenome, Humans, Monocytes, Neoplasm Proteins, Particulate Matter, Air pollution, fine particulate matter, oxides of nitrogen, DNA methylation, gene expression, Biochemistry and Cell Biology, Medical Biochemistry and Metabolomics, Developmental Biology, Biochemistry and cell biology
Abstract

Air pollution might affect atherosclerosis through DNA methylation changes in cells crucial to atherosclerosis, such as monocytes. We conducted an epigenome-wide study of DNA methylation in CD14+ monocytes and long-term ambient air pollution exposure in adults participating in the Multi-Ethnic Study of Atherosclerosis (MESA). We also assessed the association between differentially methylated signals and cis-gene expression. Using spatiotemporal models, one-year average concentrations of outdoor fine particulate matter (PM2.5) and oxides of nitrogen (NOX) were estimated at participants' homes. We assessed DNA methylation and gene expression using Illumina 450k and HumanHT-12 v4 Expression BeadChips, respectively (n = 1,207). We used bump hunting and site-specific approaches to identify differentially methylated signals (false discovery rate of 0.05) and used linear models to assess associations between differentially methylated signals and cis-gene expression. Four differentially methylated regions (DMRs) located on chromosomes 5, 6, 7, and 16 (within or near SDHAP3, ZFP57, HOXA5, and PRM1, respectively) were associated with PM2.5. The DMRs on chromosomes 5 and 6 also associated with NOX. The DMR on chromosome 5 had the smallest p-value for both PM2.5 (p = 1.4×10-6) and NOX (p = 7.7×10-6). Three differentially methylated CpGs were identified for PM2.5, and cg05926640 (near TOMM20) had the smallest p-value (p = 5.6×10-8). NOX significantly associated with cg11756214 within ZNF347 (p = 5.6×10-8). Several differentially methylated signals were also associated with cis-gene expression. The DMR located on chromosome 7 was associated with the expression of HOXA5, HOXA9, and HOXA10. The DMRs located on chromosomes 5 and 16 were associated with expression of MRPL36 and DEXI, respectively. The CpG cg05926640 was associated with expression of ARID4B, IRF2BP2, and TOMM20. We identified differential DNA methylation in monocytes associated with long-term air pollution exposure. Methylation signals associated with gene expression might help explain how air pollution contributes to cardiovascular disease.

Published
2022

45. A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels

Author

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, 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, Hveem, Kristian, Ridker, Paul M., Morange, Pierre-Emmanuel, Johnson, Andrew D., Kabrhel, Christopher, AlexandreTrégouët, David, Smith, Nicholas L., de Vries, Paul S., Reventun, Paula, Brown, Michael R., Heath, Adam S., Huffman, Jennifer E., Le, Ngoc-Quynh, Bebo, Allison, Temprano-Sagrera, Gerard, Raffield, Laura M., Ozel, Ayse Bilge, Thibord, Florian, Lewis, Joshua P., Rodriguez, Benjamin A. T., Polasek, Ozren, 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, Davies, Gail, Delgado, Graciela E., Klaric, Lucija, Noordam, Raymond, van Rooij, Frank, Curran, Joanne E., Wheeler, Marsha M., Osburn, William O., O'Connell, Jeffrey R., Beswick, Andrew, Kolcic, Ivana, Souto, Juan Carlos, Becker, Lewis C., Hansen, Torben, Doyle, Margaret F., Harris, Sarah E., Moissl, Angela P., Rich, Stephen S., Campbell, Harry, Stott, David J., Soria, Jose Manuel, de Maat, Moniek P. M., Brody, Lawrence C., Auer, Paul L., Ben-Shlomo, Yoav, Hayward, Caroline, Mathias, Rasika A., Kilpeläinen, Tuomas O., Lange, Leslie A., Cox, Simon R., März, Winfried, Rotter, Jerome I., Mook-Kanamori, Dennis O., Wilson, James F., van der Harst, Pim, Jukema, J. Wouter, Ikram, M. Arfan, Desch, Karl C., Sabater-Lleal, Maria, Lowenstein, Charles J., and Morrison, Alanna C.

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2024
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47. Association between health insurance cost-sharing and choice of hospital tier for cardiovascular diseases in China: a prospective cohort study

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Li, Liming, Wang, Chen, Lv, Jun, Peto, Richard, Walters, Robin, Avery, Daniel, Barnard, Maxim, Bennett, Derrick, Boxall, Ruth, Chan, Kahung, Chen, Yiping, Clarke, Johnathan, Du, Huaidong, Mohamed, Ahmed Edris, Fry, Hannah, Gilbert, Simon, Im, Pek Kei, Iona, Andri, Kakkoura, Maria, Kartsonaki, Christiana, Lam, Hubert, Lin, Kuang, Liu, James, Mazidi, Mohsen, Millwood, Iona, Morris, Sam, Nie, Qunhua, Pozaricki, Alfred, Ryder, Paul, Said, Saredo, Schmidt, Dan, Stevens, Becky, Turnbull, Iain, Wang, Baihan, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Yao, Pang, Han, Xiao, Hou, Can, Xia, Qingmei, Liu, Chao, Pei, Pei, Sun, Dianjianyi, Yu, Canqing, Chen, Naying, Liu, Duo, Tang, Zhenzhu, Chen, Ningyu, Jiang, Qilian, Lan, Jian, Li, Mingqiang, Liu, Yun, Meng, Fanwen, Meng, Jinhuai, Pan, Rong, Qin, Yulu, Wang, Ping, Wang, Sisi, Wei, Liuping, Zhou, Liyuan, Dong, Caixia, Ge, Pengfei, Ren, Xiaolan, Li, Zhongxiao, Mao, Enke, Wang, Tao, Zhang, Hui, Zhang, Xi, Chen, Jinyan, Hu, Ximin, Wang, Xiaohuan, Guo, Zhendong, Li, Huimei, Li, Yilei, Weng, Min, Wu, Shukuan, Yan, Shichun, Zou, Mingyuan, Zhou, Xue, Guo, Ziyan, Kang, Quan, Li, Yanjie, Yu, Bo, Xu, Qinai, Chang, Liang, Fan, Lei, Feng, Shixian, Zhang, Ding, Zhou, Gang, Gao, Yulian, He, Tianyou, He, Pan, Hu, Chen, Sun, Huarong, Zhang, Xukui, Chen, Biyun, Fu, Zhongxi, Huang, Yuelong, Liu, Huilin, Xu, Qiaohua, Yin, Li, Long, Huajun, Xu, Xin, Zhang, Hao, Zhang, Libo, Su, Jian, Tao, Ran, Wu, Ming, Yang, Jie, Zhou, Jinyi, Zhou, Yonglin, Hu, Yihe, Hua, Yujie, Jin, Jianrong, Liu, Fang, Liu, Jingchao, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Jun, Cheng, Liang, Du, Ranran, Gao, Ruqin, Li, Feifei, Li, Shanpeng, Liu, Yongmei, Ning, Feng, Pang, Zengchang, Sun, Xiaohui, Tian, Xiaocao, Wang, Shaojie, Zhai, Yaoming, Zhang, Hua, Hou, Wei, Lv, Silu, Wang, Junzheng, Chen, Xiaofang, Wu, Xianping, Zhang, Ningmei, Chang, Xiaoyu, Li, Jianguo, Liu, Jiaqiu, Luo, Guojin, Sun, Qiang, Zhong, Xunfu, Gong, Weiwei, Hu, Ruying, Wang, Hao, Wang, Meng, Yu, Min, Chen, Lingli, Gu, Qijun, Pan, Dongxia, Wang, Chunmei, Xie, Kaixu, Zhang, Xiaoyi, Chen, Hongyuan, Liu, Liyang, Gou, Haiyan, Wang, Xun, Ding, Jing, Zhang, Ning, Mao, Yueshi, Zhou, Shanshan, Jin, Lirong, Cheng, Xin, Lu, Yun, Chen, Li, Hao, Zilong, Xing, Xiaona, Wang, Lei, Ju, Naixin, Mao, Yiting, Li, Shuya, Du, Peng, Wang, Deren, Sun, Xiaojia, You, Shihao, Wang, Weizhi, Zhu, Yanmei, Li, Xiaojiu, Dong, Yi, Levy, Muriel, Buckell, John, Wu, Nina, Yip, Winnie, and Mihaylova, Borislava

Published
2024
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49. Causal association between snoring and stroke: a Mendelian randomization study in a Chinese population

Author

Chen, Junshi, Chen, Zhengming, Clarke, Robert, Collins, Rory, Guo, Yu, Li, Liming, Lv, Jun, Peto, Richard, Walter, Robin, Avery, Daniel, Bennett, Derrick, Boxall, Ruth, Burgess, Sue, Chan, Ka Hung, Chang, Yumei, Chen, Yiping, Clarke, Johnathan, Du, Huaidong, Mohamed, Ahmed Edris, Fairhurst-Hunter, Zammy, Fry, Hannah, Hill, Mike, Holmes, Michael, Im, Pek Kei, Iona, Andri, Kakkoura, Maria, Kartsonaki, Christiana, Kerosi, Rene, Lin, Kuang, Mazidi, Mohsen, Millwood, Iona, Morris, Sam, Nie, Qunhua, Pozarickij, Alfred, Ryder, Paul, Said, Saredo, Schmidt, Dan, Sherliker, Paul, Stevens, Becky, Turnbull, Iain, Walters, Robin, Wang, Lin, Wright, Neil, Yang, Ling, Yang, Xiaoming, Yao, Pang, Han, Xiao, Hou, Can, Pei, Pei, Liu, Chao, Yu, Canqing, Xia, Qingmei, Pang, Zengchang, Gao, Ruqin, Li, Shanpeng, Duan, Haiping, Wang, Shaojie, Liu, Yongmei, Du, Ranran, Zang, Yajing, Cheng, Liang, Tian, Xiaocao, Zhang, Hua, Zhai, Yaoming, Ning, Feng, Sun, Xiaohui, Li, Feifei, Lv, Silu, Wang, Junzheng, Hou, Wei, Sun, Wei, Yan, Shichun, Cui, Xiaoming, Wang, Chi, Wu, Zhenyuan, Li, Yanjie, Kang, Quan, Luo, Huiming, Ou, Tingting, Zheng, Xiangyang, Guo, Zhendong, Wu, Shukuan, Li, Yilei, Li, Huimei, Wu, Ming, Zhou, Yonglin, Zhou, Jinyi, Tao, Ran, Yang, Jie, Su, Jian, Liu, Fang, Zhang, Jun, Hu, Yihe, Lu, Yan, Ma, Liangcai, Tang, Aiyu, Zhang, Shuo, Jin, Jianrong, Liu, Jingchao, Lin, Mei, Lu, Zhenzhen, Zhou, Lifang, Xie, Changping, Lan, Jian, Zhu, Tingping, Liu, Yun, Wei, Liuping, Zhou, Liyuan, Chen, Ningyu, Qin, Yulu, Wang, Sisi, Wu, Xianping, Zhang, Ningmei, Chen, Xiaofang, Chang, Xiaoyu, Yuan, Mingqiang, Wu, Xia, Jiang, Wei, Liu, Jiaqiu, Sun, Qiang, Chen, Faqing, Ren, Xiaolan, Dong, Caixia, Zhang, Hui, Mao, Enke, Wang, Xiaoping, Wang, Tao, zhang, Xi, Kang, Kai, Feng, Shixian, Tian, Huizi, Fan, Lei, Li, XiaoLin, Sun, Huarong, He, Pan, Zhang, Xukui, Yu, Min, Hu, Ruying, Wang, Hao, Zhang, Xiaoyi, Cao, Yuan, Xie, Kaixu, Chen, Lingli, Shen, Dun, Li, Xiaojun, Jin, Donghui, Yin, Li, Liu, Huilin, Fu, Zhongxi, Xu, Xin, Zhang, Hao, Chen, Jianwei, Peng, Yuan, Zhang, Libo, Qu, Chan, Zhu, Yunqing, Zhuang, Zhenhuang, Sun, Dianjianyi, Millwood, Iona Y., and Walters, Robin G.

Published
2024
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